Updated intro and lmnn doc

Author: terrytangyuan

doc/source/algorithms.rst

@@ -1,14 +1,21 @@
 Large Margin Nearest Neighbor (LMNN)
 =====================================
 
-todo: Brief desciption or a link.
+LMNN learns a Mahanalobis distance metric in the kNN classification setting by semidefinite programming. The learned distance metric enforces the k-nearest neighbors to always belong to the same class while examples from different classes are separated by a large margin. This algorithm makes no assumptions about the distribution of the data.
 
 Example Code
 ------------------
-Using two different implementations:
-
+We use iris data here for all the examples:
 ::
+	from sklearn.datasets import load_iris
+
+	iris_data = load_iris()
+	self.iris_points = iris_data['data']
+	self.iris_labels = iris_data['target']
+	np.random.seed(1234)
 
+In this package, we have two different implementations of LMNN. Here we try both implementations in a for loop:
+::
 	for LMNN_cls in set((LMNN, python_LMNN)):
 	      lmnn = LMNN_cls(k=k, learn_rate=1e-6)
 	      lmnn.fit(self.iris_points, self.iris_labels, verbose=False)
@@ -18,6 +25,7 @@ Using two different implementations:
 
 References
 ------------------
+`Link Distance Metric Learning for Large Margin Nearest Neighbor Classification <http://papers.nips.cc/paper/2795-distance-metric-learning-for-large-margin-nearest-neighbor-classification>`_ Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul
 
 
 
@@ -74,3 +82,14 @@ Example Code
 References
 ------------------
 
+
+
+Relative Components Analysis (RCA)
+=====================================
+
+Example Code
+------------------
+
+References
+------------------
+

doc/source/intro.rst

@@ -1,9 +1,8 @@
-Introduction 
+metric-learn: Metric Learning in Python  
 =====================================
+|License| |PyPI version|
 
-|Travis-CI Build Status| |License|
-
-Metric Learning algorithms in Python.
+Distance metric is widely used in the machine learning literature. We used to choose a distance metric according to a priori (Euclidean Distance , L1 Distance, etc.) or according to the result of cross validation within small class of functions (e.g. choosing order of polynomial for a kernel). Actually, with priori knowledge of the data, we could learn a more suitable distance metric with metric learning techniques. metric-learn  contains implementations of the state-of-the-art algorithms for metric learning. These metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
 
 **Algorithms**
 
@@ -13,6 +12,7 @@ Metric Learning algorithms in Python.
 -  Least Squares Metric Learning (LSML)
 -  Neighborhood Components Analysis (NCA)
 -  Local Fisher Discriminant Analysis (LFDA)
+-  Relative Components Analysis (RCA)
 
 **Dependencies**
 
@@ -63,7 +63,7 @@ more complete.
 
 .. _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
+.. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg
+   :target: http://badge.fury.io/py/metric-learn
 .. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
    :target: http://badges.mit-license.org