Added intro page

Author: terrytangyuan

doc/source/conf.py

@@ -276,7 +276,7 @@
 #  dir menu entry, description, category)
 texinfo_documents = [
   (master_doc, 'metric-learn', u'metric-learn Documentation',
-   author, 'metric-learn', 'One line description of project.',
+   author, 'metric-learn', 'Metric Learning in Python.',
    'Miscellaneous'),
 ]
 
@@ -308,7 +308,7 @@
 # for small screen space, using the same theme for HTML and epub output is
 # usually not wise. This defaults to 'epub', a theme designed to save visual
 # space.
-#epub_theme = 'epub'
+epub_theme = 'epub'
 
 # The language of the text. It defaults to the language option
 # or 'en' if the language is not set.

doc/source/index.rst

@@ -11,6 +11,9 @@ Contents:
 .. toctree::
    :maxdepth: 2
 
+   intro
+   
+
 
 
 Indices and tables

doc/source/intro.rst

@@ -0,0 +1,69 @@
+Introduction 
+=====================================
+
+|Travis-CI Build Status| |License|
+
+Metric Learning algorithms in Python.
+
+**Algorithms**
+
+-  Large Margin Nearest Neighbor (LMNN)
+-  Information Theoretic Metric Learning (ITML)
+-  Sparse Determinant Metric Learning (SDML)
+-  Least Squares Metric Learning (LSML)
+-  Neighborhood Components Analysis (NCA)
+-  Local Fisher Discriminant Analysis (LFDA)
+
+**Dependencies**
+
+-  Python 2.6+
+-  numpy, scipy, scikit-learn
+-  (for running the examples only: matplotlib)
+
+**Installation/Setup**
+
+Run ``pip install metric-learn`` to download and install from PyPI.
+
+Run ``python setup.py install`` for default installation.
+
+Run ``python setup.py test`` to run all tests.
+
+**Usage**
+
+For full usage examples, see the ``test`` and ``examples`` directories.
+
+Each metric is a subclass of ``BaseMetricLearner``, which provides
+default implementations for the methods ``metric``, ``transformer``, and
+``transform``. Subclasses must provide an implementation for either
+``metric`` or ``transformer``.
+
+For an instance of a metric learner named ``foo`` learning from a set of
+``d``-dimensional points, ``foo.metric()`` returns a ``d`` by ``d``
+matrix ``M`` such that a distance between vectors ``x`` and ``y`` is
+expressed ``(x-y).dot(M).dot(x-y)``.
+
+In the same scenario, ``foo.transformer()`` returns a ``d`` by ``d``
+matrix ``L`` such that a vector ``x`` can be represented in the learned
+space as the vector ``L.dot(x)``.
+
+For convenience, the function ``foo.transform(X)`` is provided for
+converting a matrix of points (``X``) into the learned space, in which
+standard Euclidean distance can be used.
+
+**Notes**
+
+If a recent version of the Shogun Python modular (``modshogun``) library
+is available, the LMNN implementation will use the fast C++ version from
+there. The two implementations differ slightly, and the C++ version is
+more complete.
+
+**TODO**
+
+- implement the rest of the methods on `this site`_
+
+.. _this site: http://www.cs.cmu.edu/~liuy/distlearn.htm
+
+.. |Travis-CI Build Status| image:: https://api.travis-ci.org/all-umass/metric-learn.svg?branch=master
+   :target: https://travis-ci.org/all-umass/metric-learn
+.. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
+   :target: http://badges.mit-license.org