This small Python library implements basic functions to perform trajectory inference with Gaussian processes. It is still in development phase.
The core classes and functions are defined in the gp_code directory.
We recommend using a virtual environment:
$ python3 -m venv ./venv
$ source ./venv/bin/activate
$ pip install -r requirements.txt
You can visualize the effect of different kernels with the kernelVisualization script:
$ python3 examples/test_kernelVisualization.py
$ python3 examples/test_train.py
To display the mixture predictive distribution at a certain point, from past observations
$ python3 examples/test_mixtureGP.py
To get an animation of the updates of one single trajectory conditioned on a fixed goal
$ python3 examples/test_animation_single_trajectory.py
To get an animation of the updates of the trajectories
$ python3 examples/test_animation_multi.py