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# Geomstats

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NEWS: The white paper summarizing the findings from our `ICLR 2021 challenge of computational differential geometry and topology <https://gt-rl.github.io/challenge>` is out. `Read it here <https://arxiv.org/abs/2108.09810>`.

Geomstats is an open-source Python package for computations and statistics on manifolds. The package is organized into two main modules: `geometry` and `learning`.

The module `geometry` implements concepts in differential geometry, and the module `learning` implements statistics and learning algorithms for data on manifolds.

.. raw:: html

``````<img src="https://raw.githubusercontent.com/ninamiolane/geomstats/master/examples/imgs/h2_grid.png" height="120px" width="120px" align="left">
``````
• To get an overview of `geomstats`, see our `introductory video <https://www.youtube.com/watch?v=Ju-Wsd84uG0&list=PLYx7XA2nY5GejOB1lsvriFeMytD1-VS1B&index=3>`__.
• To get started with `geomstats`, see the `examples <https://github.com/geomstats/geomstats/tree/master/examples>` and `notebooks <https://github.com/geomstats/geomstats/tree/master/notebooks>` directories.
• The documentation of `geomstats` can be found on the `documentation website <https://geomstats.github.io/>`__.
• To follow the scientific literature on geometric statistics, follow our twitter-bot `@geomstats-papers <https://twitter.com/geomstats>`__!

If you find `geomstats` useful, please kindly cite our `paper <https://jmlr.org/papers/v21/19-027.html>`__:

::

``````@article{JMLR:v21:19-027,
author  = {Nina Miolane and Nicolas Guigui and Alice Le Brigant and Johan Mathe and Benjamin Hou and Yann Thanwerdas and Stefan Heyder and Olivier Peltre and Niklas Koep and Hadi Zaatiti and Hatem Hajri and Yann Cabanes and Thomas Gerald and Paul Chauchat and Christian Shewmake and Daniel Brooks and Bernhard Kainz and Claire Donnat and Susan Holmes and Xavier Pennec},
title   = {Geomstats:  A Python Package for Riemannian Geometry in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2020},
volume  = {21},
number  = {223},
pages   = {1-9},
url     = {http://jmlr.org/papers/v21/19-027.html}
}
``````

## Install geomstats via pip3

From a terminal (OS X & Linux), you can install geomstats and its requirements with `pip3` as follows:

::

``````pip3 install geomstats
``````

This method installs the latest version of geomstats that is uploaded on PyPi. Note that geomstats is only available with Python3.

## Install geomstats via Git

From a terminal (OS X & Linux), you can install geomstats and its requirements via `git` as follows:

::

``````git clone https://github.com/geomstats/geomstats.git
pip3 install -r requirements.txt
``````

This method installs the latest GitHub version of geomstats. Developers should install this version, together with the development requirements and the optional requirements to enable `tensorflow` and `pytorch` backends:

::

``````pip3 install -r dev-requirements.txt -r opt-requirements.txt
``````

To add the `requirements.txt` into a conda environment, you can use the `enviroment.yml` file as follows:

::

conda env create --file environment.yml

Note that this only installs the minimum requirements. To add the optional, development, continuous integration and documentation requirements, refer to the files `*-requirements.txt`.

## Choose the backend

Geomstats can run seamlessly with `numpy`, `tensorflow` or `pytorch`. Note that `pytorch` and `tensorflow` requirements are optional, as geomstats can be used with `numpy` only. By default, the `numpy` backend is used. The visualizations are only available with this backend.

To get the `tensorflow` and `pytorch` versions compatible with geomstats, install the `optional requirements <https://github.com/geomstats/geomstats/blob/master/opt-requirements.txt>`__:

::

``````pip3 install -r opt-requirements.txt
``````

You can choose your backend by setting the environment variable `GEOMSTATS_BACKEND` to `numpy`, `tensorflow` or `pytorch`, and importing the `backend` module. From the command line:

::

``````export GEOMSTATS_BACKEND=pytorch
``````

and in the Python3 code:

::

``````import geomstats.backend as gs
``````

## Getting started

To use `geomstats` for learning algorithms on Riemannian manifolds, you need to follow three steps: - instantiate the manifold of interest,

• instantiate the learning algorithm of interest, - run the algorithm.

The data should be represented by a `gs.array`. This structure represents numpy arrays, or tensorflow/pytorch tensors, depending on the choice of backend.

The following code snippet shows the use of tangent Principal Component Analysis on simulated `data` on the space of 3D rotations.

.. code:: python

``````from geomstats.geometry.special_orthogonal import SpecialOrthogonal
from geomstats.learning.pca import TangentPCA

so3 = SpecialOrthogonal(n=3, point_type='vector')
metric = so3.bi_invariant_metric

data = so3.random_uniform(n_samples=10)

tpca = TangentPCA(metric=metric, n_components=2)
tpca = tpca.fit(data)
tangent_projected_data = tpca.transform(data)
``````

All geometric computations are performed behind the scenes. The user only needs a high-level understanding of Riemannian geometry. Each algorithm can be used with any of the manifolds and metric implemented in the package.

To see additional examples, go to the `examples <https://github.com/geomstats/geomstats/tree/master/examples>` or `notebooks <https://github.com/geomstats/geomstats/tree/master/notebooks>` directories.

## Contributing

See our `contributing <https://github.com/geomstats/geomstats/blob/master/docs/contributing.rst>`__ guidelines!

## Acknowledgements

This work is supported by:

• the Inria-Stanford associated team `GeomStats <http://www-sop.inria.fr/asclepios/projects/GeomStats/>`__,
• the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement `G-Statistics <https://team.inria.fr/epione/en/research/erc-g-statistics/>`__ No. 786854),
• the French society for applied and industrial mathematics (`SMAI <http://smai.emath.fr/>`__),
• the National Science Foundation (grant NSF DMS RTG 1501767).

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