Inputdsa

.gitignore

@@ -0,0 +1,63 @@
+# Python cache files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

DSA/__init__.py

@@ -1,8 +1,15 @@
-from DSA.dsa import DSA
+__version__ = "2.0.0"
+
+from DSA.dsa import DSA, ControllabilitySimilarityTransformDistConfig, GeneralizedDSA, InputDSA, SimilarityTransformDistConfig
+from DSA.dsa import DefaultDMDConfig as DMDConfig
+from DSA.dsa import pyKoopmanDMDConfig, SubspaceDMDcConfig, DMDcConfig
+from DSA.dsa import SimilarityTransformDistConfig, ControllabilitySimilarityTransformDistConfig
 from DSA.dmd import DMD
-from DSA.kerneldmd import KernelDMD
+from DSA.dmdc import DMDc
+from DSA.subspace_dmdc import SubspaceDMDc
 from DSA.simdist import SimilarityTransformDist
+from DSA.simdist_controllability import ControllabilitySimilarityTransformDist
 from DSA.stats import *
-from DSA.sweeps import *
+from DSA.sweeps import PyKoopmanSweeper, DefaultSweeper
 from DSA.preprocessing import *
-from DSA.resdmd import *
+from DSA.resdmd import ResidualComputer

DSA/base_dmd.py

@@ -0,0 +1,282 @@
+"""Base class for DMD implementations."""
+
+import numpy as np
+import torch
+import warnings
+from abc import ABC, abstractmethod
+
+
+class BaseDMD(ABC):
+    """Base class for DMD implementations with common functionality."""
+
+    def __init__(
+        self,
+        device="cpu",
+        verbose=False,
+        send_to_cpu=False,
+        lamb=0,
+    ):
+        """
+        Parameters
+        ----------
+        device: string, int, or torch.device
+            A string, int or torch.device object to indicate the device to torch.
+            If 'cuda' or 'cuda:X' is specified but not available, will fall back to 'cpu' with a warning.
+        verbose: bool
+            If True, print statements will be provided about the progress of the fitting procedure.
+        send_to_cpu: bool
+            If True, will send all tensors in the object back to the cpu after everything is computed.
+            This is implemented to prevent gpu memory overload when computing multiple DMDs.
+        lamb : float
+            Regularization parameter for ridge regression. Defaults to 0.
+        """
+        self.device = device
+        self.verbose = verbose
+        self.send_to_cpu = send_to_cpu
+        self.lamb = lamb
+
+        # Common attributes
+        self.data = None
+        self.n = None
+        self.ntrials = None
+        self.is_list_data = False
+
+        # SVD attributes - will be set by subclasses
+        self.cumulative_explained_variance = None
+
+    def _setup_device(self, device='cpu', use_torch=None):
+        """
+        Smart device setup with graceful fallback and auto-detection.
+
+        Parameters
+        ----------
+        device : str or torch.device
+            Requested device ('cpu', 'cuda', 'cuda:0', etc.)
+        use_torch : bool or None
+            Whether to use PyTorch. If None, auto-detected:
+            - True if device contains 'cuda'
+            - False otherwise (numpy is faster on CPU)
+
+        Returns
+        -------
+        tuple
+            (device, use_torch) - validated device and use_torch flag
+        """
+        # Convert device to string for checking
+        device_str = str(device).lower()
+
+        # Auto-detect use_torch if not specified
+        if use_torch is None:
+            use_torch = 'cuda' in device_str
+
+        # If CUDA requested, check availability
+        if 'cuda' in device_str:
+            if not torch.cuda.is_available():
+                warnings.warn(
+                    f"CUDA device '{device}' requested but CUDA is not available. "
+                    "Falling back to CPU. "
+                    "To use GPU acceleration, ensure PyTorch with CUDA support is installed.",
+                    RuntimeWarning,
+                    stacklevel=3
+                )
+                device = 'cpu'
+                use_torch = False  # Use numpy on CPU for better performance
+            else:
+                # CUDA is available, verify the specific device exists
+                try:
+                    test_device = torch.device(device)
+                    # Test if we can actually use this device
+                    torch.tensor([1.0], device=test_device)
+                    use_torch = True
+                except (RuntimeError, AssertionError) as e:
+                    warnings.warn(
+                        f"CUDA device '{device}' requested but not accessible: {e}. "
+                        f"Falling back to CPU.",
+                        RuntimeWarning,
+                        stacklevel=3
+                    )
+                    device = 'cpu'
+                    use_torch = False
+
+        # Convert to torch.device if using torch
+        if use_torch:
+            device = torch.device(device)
+        else:
+            device = None  # Use numpy (no torch device needed)
+
+        return device, use_torch
+
+    def _process_single_dataset(self, data):
+        """Process a single dataset, handling numpy arrays, tensors, and lists."""
+        if isinstance(data, list):
+            try:
+                # Attempt to convert to a single tensor if possible (non-ragged)
+                processed_data = [
+                    torch.from_numpy(d) if isinstance(d, np.ndarray) else d
+                    for d in data
+                ]
+                return torch.stack(processed_data), False
+            except (RuntimeError, ValueError):
+                # Handle ragged lists
+                processed_data = [
+                    torch.from_numpy(d) if isinstance(d, np.ndarray) else d
+                    for d in data
+                ]
+                # Check for consistent last dimension
+                n_features = processed_data[0].shape[-1]
+                if not all(d.shape[-1] == n_features for d in processed_data):
+                    raise ValueError(
+                        "All tensors in the list must have the same number of features (last dimension)."
+                    )
+                return processed_data, True
+
+        elif isinstance(data, np.ndarray):
+            return torch.from_numpy(data.copy()), False
+
+        return data, False
+
+    def _init_single_data(self, data):
+        """Initialize data attributes for a single dataset."""
+        processed_data, is_ragged = self._process_single_dataset(data)
+
+        if is_ragged:
+            # Set attributes for ragged data
+            n_features = processed_data[0].shape[-1]
+            self.n = n_features
+            self.ntrials = sum(d.shape[0] if d.ndim == 3 else 1 for d in processed_data)
+            self.trial_counts = [
+                d.shape[0] if d.ndim == 3 else 1 for d in processed_data
+            ]
+            self.is_list_data = True
+        else:
+            # Set attributes for non-ragged data
+            if processed_data.ndim == 3:
+                self.ntrials = processed_data.shape[0]
+                self.n = processed_data.shape[2]
+            else:
+                self.n = processed_data.shape[1]
+                self.ntrials = 1
+            self.is_list_data = False
+
+        return processed_data
+
+    def _compute_explained_variance(self, S):
+        """Compute cumulative explained variance from singular values."""
+        exp_variance = S**2 / torch.sum(S**2)
+        return torch.cumsum(exp_variance, 0)
+
+    def _compute_rank_from_params(
+        self,
+        S,
+        cumulative_explained_variance,
+        max_rank,
+        rank=None,
+        rank_thresh=None,
+        rank_explained_variance=None,
+    ):
+        """
+        Compute rank based on provided parameters.
+
+        Parameters
+        ----------
+        S : torch.Tensor
+            Singular values
+        cumulative_explained_variance : torch.Tensor
+            Cumulative explained variance
+        max_rank : int
+            Maximum possible rank
+        rank : int, optional
+            Explicit rank specification
+        rank_thresh : float, optional
+            Threshold for singular values
+        rank_explained_variance : float, optional
+            Explained variance threshold
+
+        Returns
+        -------
+        int
+            Computed rank
+        """
+        parameters_provided = [
+            rank is not None,
+            rank_thresh is not None,
+            rank_explained_variance is not None,
+        ]
+        num_parameters_provided = sum(parameters_provided)
+
+        if num_parameters_provided > 1:
+            raise ValueError(
+                "More than one rank parameter was provided. Please provide only one of rank, rank_thresh, or rank_explained_variance."
+            )
+        elif num_parameters_provided == 0:
+            computed_rank = len(S)
+        else:
+            if rank is not None:
+                computed_rank = rank
+            elif rank_thresh is not None:
+                # Find the number of singular values greater than the threshold
+                computed_rank = int((S > rank_thresh).sum().item())
+                if computed_rank == 0:
+                    computed_rank = 1  # Ensure at least rank 1
+            elif rank_explained_variance is not None:
+                cumulative_explained_variance_cpu = (
+                    cumulative_explained_variance.cpu().numpy()
+                )
+                computed_rank = int(
+                    np.searchsorted(
+                        cumulative_explained_variance_cpu, rank_explained_variance
+                    )
+                    + 1
+                )
+                if computed_rank > len(S):
+                    computed_rank = len(S)
+
+        # Ensure rank doesn't exceed maximum possible
+        if computed_rank > max_rank:
+            computed_rank = max_rank
+
+        return computed_rank
+
+    def _to_torch(self, x):
+        """Convert numpy array to torch tensor on the appropriate device."""
+        if not self.use_torch or x is None:
+            return x
+        if isinstance(x, torch.Tensor):
+            return x.to(self.device)
+        return torch.from_numpy(x).to(self.device)
+
+    def _to_numpy(self, x):
+        """Convert torch tensor to numpy array."""
+        if not self.use_torch or x is None:
+            return x
+        if isinstance(x, torch.Tensor):
+            return x.cpu().numpy()
+        return x
+
+    def all_to_device(self, device="cpu"):
+        """Move all tensor attributes to specified device."""
+        for k, v in self.__dict__.items():
+            if isinstance(v, torch.Tensor):
+                self.__dict__[k] = v.to(device)
+            elif isinstance(v, list) and len(v) > 0 and isinstance(v[0], torch.Tensor):
+                self.__dict__[k] = [tensor.to(device) for tensor in v]
+
+    @abstractmethod
+    def fit(self, *args, **kwargs):
+        """Fit the DMD model. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def predict(self, *args, **kwargs):
+        """Make predictions with the DMD model. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def compute_hankel(self, *args, **kwargs):
+        """Compute Hankel matrix. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def compute_svd(self, *args, **kwargs):
+        """Compute SVD. Must be implemented by subclasses."""
+        pass