DOC minor fixes to rst and image paths

doc/datasets/index.rst

@@ -108,8 +108,8 @@ Sample generators
 In addition, scikit-learn includes various random sample generators that
 can be used to build artifical datasets of controled size and complexity.
 
-.. image:: ../auto_examples/images/plot_random_dataset_1.png
-   :target: ../auto_examples/plot_random_dataset.html
+.. image:: ../auto_examples/datasets/images/plot_random_dataset_1.png
+   :target: ../auto_examples/datasets/plot_random_dataset.html
    :scale: 50
    :align: center
 

doc/modules/pipeline.rst

@@ -28,6 +28,7 @@ Usage
 The :class:`Pipeline` is build using a list of ``(key, value)`` pairs, where
 the ``key`` a string containing the name you want to give this step and ``value``
 is an estimator object::
+
     >>> from sklearn.pipeline import Pipeline
     >>> from sklearn.svm import SVC
     >>> from sklearn.decomposition import PCA
@@ -40,21 +41,25 @@ is an estimator object::
         shrinking=True, tol=0.001, verbose=False))])
 
 The estimators of the pipeline are stored as a list in the ``steps`` attribute::
+
     >>> clf.steps[0]
     ('reduce_dim', PCA(copy=True, n_components=None, whiten=False))
 
 and as a ``dict`` in ``named_steps``::
+
     >>> clf.named_steps['reduce_dim']
     PCA(copy=True, n_components=None, whiten=False)
 
 Parameters of the estimators in the pipeline can be accessed using the
 ``<estimator>__<parameter>`` syntax::
+
     >>> clf.set_params(svm__C=10) # NORMALIZE_WHITESPACE
     Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None, whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
       kernel='rbf', probability=False, shrinking=True, tol=0.001,
       verbose=False))])
 
 This is particularly important for doing grid searches::
+
     >>> from sklearn.grid_search import GridSearchCV
     >>> params = dict(reduce_dim__n_components=[2, 5, 10],
     ...               svm__C=[0.1, 10, 100])

doc/presentations.rst

@@ -13,12 +13,14 @@ Videos
 - `Introduction to scikit-learn
   <http://videolectures.net/icml2010_varaquaux_scik/>`_ by `Gael Varoquaux`_ at
   ICML 2010
+
     A three minute video from a very early stage of the scikit, explaining the
     basic idea and approach we are following.
 
 - `Introduction to statistical learning with scikit
   learn <http://archive.org/search.php?query=scikit-learn>`_ by
   `Gael Varoquaux`_ at SciPy 2011
+
     An extensive tutorial, consisting of four sessions of one hour.
     The tutorial covers basics of machine learning,
     many algorithms and how to apply them using scikit-learn. The
@@ -36,10 +38,12 @@ Videos
 
 - `Introduction to Interactive Predictive Analytics in Python with scikit-learn <http://www.youtube.com/watch?v=Zd5dfooZWG4>`_
   by `Olivier Grisel`_ at PyCon 2012
+
     3-hours long introduction to prediction tasks using the scikit-learn.
 
 - `scikit-learn - Machine Learning in Python <http://marakana.com/s/scikit-learn_machine_learning_in_python,1152/index.html>`_
   by `Jake Vanderplas`_ at the 2012 PyData workshop at Google
+
     Interactive demonstration of some scikit-learn features. 75 minutes.
 
 .. _Gael Varoquaux: http://gael-varoquaux.info

doc/tutorial/basic/tutorial.rst

@@ -179,8 +179,8 @@ which we have not used to train the classifier::
 
 The corresponding image is the following:
 
-.. image:: ../../auto_examples/images/datasets/plot_digits_last_image_1.png
-    :target: ../../auto_examples/plot_digits_last_image.html
+.. image:: ../../auto_examples/datasets/images/plot_digits_last_image_1.png
+    :target: ../../auto_examples/datasets/plot_digits_last_image.html
     :align: center
     :scale: 50
 

doc/tutorial/statistical_inference/settings.rst

@@ -31,8 +31,8 @@ needs to be preprocessed to be used by the scikit.
 
 .. topic:: An example of reshaping data: the digits dataset 
 
-    .. image:: ../../auto_examples/images/datasets/plot_digits_last_image_1.png
-        :target: ../../auto_examples/plot_digits_last_image.html
+    .. image:: ../../auto_examples/datasets/images/plot_digits_last_image_1.png
+        :target: ../../auto_examples/datasets/plot_digits_last_image.html
         :align: right
         :scale: 60
 

doc/tutorial/statistical_inference/supervised_learning.rst

@@ -39,8 +39,8 @@ Nearest neighbor and the curse of dimensionality
 
 .. topic:: Classifying irises:
    
-    .. image:: ../../auto_examples/images/datasets/plot_iris_dataset_1.png
-        :target: ../../auto_examples/plot_iris_dataset.html
+    .. image:: ../../auto_examples/datasets/images/plot_iris_dataset_1.png
+        :target: ../../auto_examples/datasets/plot_iris_dataset.html
         :align: right
 	:scale: 65
 
@@ -546,8 +546,8 @@ creating an decision energy by positioning *kernels* on observations:
    `svm_gui.py`; add data points of both classes with right and left button, 
    fit the model and change parameters and data.
 
-.. image:: ../../auto_examples/images/datasets/plot_iris_dataset_1.png
-    :target: ../../auto_examples/plot_iris_dataset.html
+.. image:: ../../auto_examples/datasets/images/plot_iris_dataset_1.png
+    :target: ../../auto_examples/datasets/plot_iris_dataset.html
     :align: right
     :scale: 70