Metadata-Version: 2.1
Name: geomstats
Version: 2.2.1
Summary: Geometric statistics on manifolds
Home-page: http://github.com/geomstats/geomstats
Author: Nina Miolane
Author-email: ninamio78@gmail.com
License: MIT
Description: # Geomstats
        [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/geomstats-papers.svg?style=social&label=Follow%20%40geomstats-papers)](https://twitter.com/geomstats)[![PyPI version](https://badge.fury.io/py/geomstats.svg)](https://badge.fury.io/py/geomstats)[![Build Status](https://travis-ci.org/geomstats/geomstats.svg?branch=master)](https://travis-ci.org/geomstats/geomstats)[![Slack](https://img.shields.io/badge/Slack-Join-yellow)](https://geomstats.slack.com/)[![Coverage Status](https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=numpy)](https://codecov.io/gh/geomstats/geomstats)[![Coverage Status](https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=tensorflow)](https://codecov.io/gh/geomstats/geomstats)[![Coverage Status](https://codecov.io/gh/geomstats/geomstats/branch/master/graph/badge.svg?flag=pytorch)](https://codecov.io/gh/geomstats/geomstats) (Coverages for: numpy, tensorflow, pytorch)
        
        
        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.
        
        <img align="left" src="https://raw.githubusercontent.com/ninamiolane/geomstats/master/examples/imgs/h2_grid.png" width=120 height=120>
        
        
        - 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/).
        - If you find ``geomstats`` useful, please kindly cite our [paper](https://jmlr.org/papers/v21/19-027.html).
        - To follow the scientific literature on geometric statistics, follow our twitter-bot @geomstats-papers !
        
        ## 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
        ```
        
        ## Choose the backend
        
        Geomstats can run seemlessly 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.
        
        ```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).
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: all
