scikit-learn
diff --git a/‎doc/tutorial/statistical_inference/unsupervised_learning.rst
Lines changed: 18 additions & 18 deletions b/‎doc/tutorial/statistical_inference/unsupervised_learning.rst
Lines changed: 18 additions & 18 deletions
@@ -12,16 +12,16 @@ Clustering: grouping observations together
     **clustering task**: split the observations into well-separated group
     called *clusters*.
 
-..
-   >>> # Set the PRNG
+..   
+   >>> # Set the PRNG   
    >>> import numpy as np
    >>> np.random.seed(1)
 
 K-means clustering
 -------------------
 
 Note that there exist a lot of different clustering criteria and associated
-algorithms. The simplest clustering algorithm is
+algorithms. The simplest clustering algorithm is 
 :ref:`k_means`.
 
 .. image:: ../../auto_examples/cluster/images/plot_cluster_iris_002.png
@@ -30,7 +30,7 @@ algorithms. The simplest clustering algorithm is
     :align: right
 
 
-::
+:: 
 
     >>> from sklearn import cluster, datasets
     >>> iris = datasets.load_iris()
@@ -57,30 +57,30 @@ algorithms. The simplest clustering algorithm is
    :target: ../../auto_examples/cluster/plot_cluster_iris.html
    :scale: 63
 
-.. warning::
-
+.. warning:: 
+   
     There is absolutely no guarantee of recovering a ground truth. First,
     choosing the right number of clusters is hard. Second, the algorithm
     is sensitive to initialization, and can fall into local minima,
     although scikit-learn employs several tricks to mitigate this issue.
 
     .. list-table::
         :class: centered
-
-        *
-
+        
+        * 
+        
             - |k_means_iris_bad_init|
 
             - |k_means_iris_8|
 
             - |cluster_iris_truth|
 
-        *
-
+        * 
+        
             - **Bad initialization**
-
+            
             - **8 clusters**
-
+            
             - **Ground truth**
 
     **Don't over-interpret clustering results**
@@ -105,8 +105,8 @@ algorithms. The simplest clustering algorithm is
 
     Clustering in general and KMeans, in particular, can be seen as a way
     of choosing a small number of exemplars to compress the information.
-    The problem is sometimes known as
-    `vector quantization <http://en.wikipedia.org/wiki/Vector_quantization>`_.
+    The problem is sometimes known as 
+    `vector quantization <http://en.wikipedia.org/wiki/Vector_quantization>`_. 
     For instance, this can be used to posterize an image::
 
         >>> import scipy as sp
@@ -125,7 +125,7 @@ algorithms. The simplest clustering algorithm is
     	>>> lena_compressed.shape = lena.shape
 
     .. list-table::
-      :class: centered
+      :class: centered 
 
       *
         - |lena|
@@ -275,8 +275,7 @@ data by projecting on a principal subspace.
     >>> from sklearn import decomposition
     >>> pca = decomposition.PCA()
     >>> pca.fit(X)
-    PCA(copy=True, iterated_power=3, n_components=None, random_state=None,
-      svd_solver='auto', tol=0.0, whiten=False)
+    PCA(copy=True, n_components=None, whiten=False)
     >>> print(pca.explained_variance_)  # doctest: +SKIP
     [  2.18565811e+00   1.19346747e+00   8.43026679e-32]
 
@@ -321,3 +320,4 @@ a maximum amount of independent information. It is able to recover
     >>> A_ = ica.mixing_.T
     >>> np.allclose(X,  np.dot(S_, A_) + ica.mean_)
     True
+