MAINT/DOC Fix sphinx warnings (#104)

Author: rth

doc/modules/cluster.rst

@@ -4,11 +4,11 @@
 Clustering with KMedoids and Common-nearest-neighbors
 =====================================================
 .. _k_medoids:
-.. currentmodule:: sklearn_extra.cluster
 
 K-Medoids
 =========
 
+.. currentmodule:: sklearn_extra.cluster
 
 :class:`KMedoids` is related to the :class:`KMeans <sklearn.cluster.KMeans>` algorithm. While
 :class:`KMeans <sklearn.cluster.KMeans>` tries to minimize the within cluster sum-of-squares,

doc/modules/kernel_approximation.rst

@@ -3,7 +3,6 @@
 ==================================================
 Kernel map approximation for faster kernel methods
 ==================================================
-.. _kernel_approximation:
 
 .. currentmodule:: sklearn_extra.kernel_approximation
 

examples/plot_kmedoids.py

@@ -4,10 +4,9 @@
 KMedoids Demo
 =============
 
-KMedoids clustering of data points. The goal is to find medoids than minimize the
-sum of absolute distance to the closest medoid. A medoid is a point of the dataset.
-Read more in the :ref:`User Guide
-<_k_medoids>`.
+KMedoids clustering of data points. The goal is to find medoids than minimize
+the sum of absolute distance to the closest medoid. A medoid is a point of the
+dataset.  Read more in the :ref:`User Guide <_k_medoids>`.
 
 """
 import matplotlib.pyplot as plt
@@ -31,7 +30,7 @@
 cobj = KMedoids(n_clusters=3).fit(X)
 labels = cobj.labels_
 
-############################################################"""
+##############################################################
 # Plot results
 unique_labels = set(labels)
 colors = [

examples/plot_robust_classification_toy.py

@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """
-=============================================================
+==============================================================
 A demo of Robust Classification on Simulated corrupted dataset
-=============================================================
+==============================================================
 In this example we compare the RobustWeightedClassifier using SGDClassifier
 for classification with the vanilla SGDClassifier with various losses.
 """