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pyRiemann: Machine learning for multivariate data with Riemannian geometry
==========================================================================

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pyRiemann is a Python machine learning package based on scikit-learn API.
It provides a high-level interface for processing and classification of real (resp. complex)-valued multivariate data
through the Riemannian geometry of symmetric (resp. Hermitian) positive definite (SPD) (resp. HPD) matrices.

For a brief introduction to the ideas behind the package, you can read the
:ref:`introductory notes <introduction>`. More practical information is on the
:ref:`installation page <installing>`. You may also want to browse the
`example gallery <auto_examples/index.html>`_ to get a sense for what you can do with pyRiemann
and :ref:`API reference <api_ref>` to find out how.

To see the code or report a bug, please visit the `github repository
<https://github.com/pyRiemann/pyRiemann>`_.

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   Introduction <introduction>
   Release notes <whatsnew>
   Installing <installing>
   Example gallery <auto_examples/index>
   API reference <api>

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