Skip to content
Browse files

Address review

  • Loading branch information
ninamiolane committed Feb 28, 2020
1 parent 7be02fd commit 5984e071804b6b60f624e04756ca27e220b6a5cc
Showing with 7 additions and 7 deletions.
  1. +5 −5 docs/first-steps.rst
  2. +2 −2 docs/index.rst
@@ -6,22 +6,22 @@ First steps

The purpose of this guide is to illustrate the possible uses of geomstats.

**Install geomstats.**

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

pip3 install -r requirements.txt
pip3 install geomstats

**Choose the backend.**

You can choose your backend, by setting the environment variable GEOMSTATS_BACKEND to numpy, tensorflow or pytorch. By default, numpy is used. You should only use the numpy backend for examples with visualizations.
You can choose your backend by setting the environment variable ``GEOMSTATS_BACKEND`` to ``numpy``, ``tensorflow`` or ``pytorch``. By default, the numpy backend is used. You should only use the numpy backend for examples with visualizations.

.. code-block:: bash
**First examples.**

We illustrate here the use of `geomstats` to generalize learning
algorithms to Riemannian manifolds.
@@ -58,7 +58,7 @@ Each algorithm can be used with any of the manifolds and metric
implemented in the package.

**Next steps.**

You can find more examples in the repository "examples" of geomstats. You can run them from the command line as follows.

@@ -11,12 +11,12 @@ such as manifolds and Riemannian metrics, with an object-oriented approach.

The module `learning` implements statistics and learning algorithms for data
on manifolds. The code is object-oriented and classes inherit from
Scikit-Learn base classes and mixin.
scikit-learn's base classes and mixins.

To learn how to use `geomstats`, visit the page :ref:`first_steps`.
To contribute to `geomstats` visit the page :ref:`contributing`.

**Quick install**

.. code-block:: bash

0 comments on commit 5984e07

Please sign in to comment.
You can’t perform that action at this time.