converting to RST format, renaming to metric-learn

Author: perimosocordiae

README.rst

@@ -1,54 +1,66 @@
-[![Travis-CI Build Status](https://api.travis-ci.org/all-umass/metric_learn.svg?branch=master)](https://travis-ci.org/all-umass/metric_learn)
-[![License](http://img.shields.io/:license-mit-blue.svg?style=flat)](http://badges.mit-license.org)
+|Travis-CI Build Status| |License|
 
-# metric_learn
+metric-learn
+=============
 
 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)
+-  Large Margin Nearest Neighbor (LMNN)
+-  Information Theoretic Metric Learning (ITML)
+-  Sparse Determinant Metric Learning (SDML)
+-  Least Squares Metric Learning (LSML)
+-  Neighborhood Components Analysis (NCA)
 
 **Dependencies**
 
- * Python 2.6+
- * numpy, scipy, scikit-learn
- * (for running the examples only: matplotlib)
+-  Python 2.6+
+-  numpy, scipy, scikit-learn
+-  (for running the examples only: matplotlib)
 
 **Installation/Setup**
 
-Run `python setup.py install` for default installation.
+Run ``python setup.py install`` for default installation.
 
-Run `python setup.py test` to run all tests.
+Run ``python setup.py test`` to run all tests.
 
 **Usage**
 
-For full usage examples, see the `test` and `examples` directories.
+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`.
+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)`.
+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)`.
+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.
+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.
+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](http://www.cs.cmu.edu/~liuy/distlearn.htm)*
+**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