Regression with covariance matrices
===================================

This is the Python code for the NeurIPS 2019 submission xxx


Dependencies
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 - numpy >= 1.15
 - scipy >= 1.12
 - matplotlib >= 3.0
 - scikit-learn >= 0.20
 - pyriemann (https://github.com/alexandrebarachant/pyRiemann)


The file generation.py contains the functions to generate covariance matrices following the generative model of the paper.
The file wasserstein_tangent.py implements the vectorization with the Wasserstein distance.
Vectorization using the geometric distance is performed using the PyRiemann package.


The SNR experiment is obtained by running the script snr_experiment.py
