Conditional Backward/Forward SSM
This repository contains the official implementation of the CBF-SSM model presented in Structured Variational Inference in Unstable Gaussian Process State Space Models by Silvan Melchior, Felix Berkenkamp, Sebastian Curi, Andreas Krause.
Please cite the above paper when using this code in any way.
The datasets PR-SSM was already benchmarked on (Actuator, Ballbeam, Drive, Dryer, Furnace, Sarcos) can be downloaded as described in the readme in their repo.
The remaining datasets (RoboMove, Voliro, SpringNonLinear) can be downloaded here.
All datasets need to be placed in cbfssm/datasets/data.
To install CBF-SSM, run:
$ cd <path-of-repo> $ pip3 install -e .
Reproduce Paper Results
The folder run contains a script to reproduce the results for every dataset we use to compare CBF-SSM to previous work. The results will be in a new folder called run_output.
Run Your Own Experiments
Follow these instructions to run your own experiments using CBF-SSM
- loads the data
- normalizes the data
- saves the data as train- and test-arrays with shape
[experiments, time-samples, data-dimension]
Loading of the data depends on the source of your new dataset. For normalizing the data, there are helper functions if you have one experiment only (i.e. one long sequence), again see example.
Then, write a new run-file. You can use the template as a starting point, which also contains a lot of comments on how to choose your parameters.