DOC: finish misc in tutorial

@jaquesgrobler : there were still a few things to do :)

doc/tutorial/statistical_inference/diabetes_cv_exercise.rst

@@ -1,10 +0,0 @@
-
-Exercise: setting sparsity on diabetes 
-========================================
-
-.. literalinclude:: ../../auto_examples/exercises/plot_cv_diabetes.py
-    :lines: 1-13
-
-Solution: :download:`../../auto_examples/exercises/plot_cv_diabetes.py`
-
-

doc/tutorial/statistical_inference/digits_classification_exercise.rst

@@ -1,9 +0,0 @@
-
-Excercice: classification of digits
-====================================
-
-.. literalinclude:: ../../auto_examples/exercises/plot_digits_classification_exercise.py
-    :lines: 1-5   
-
-Solution: :download:`../../auto_examples/exercises/plot_digits_classification_exercise.py`
-

doc/tutorial/statistical_inference/digits_cv_exercise.rst

@@ -1,11 +0,0 @@
-.. _digits_cv_ex:
-
-Exercise: model selection on digits
-=====================================
-
-.. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
-    :lines: 1-9 
-
-Solution: :download:`../../auto_examples/exercises/plot_cv_digits.py`
-
-

doc/tutorial/statistical_inference/index.rst

@@ -26,8 +26,6 @@ Statistical-learning for scientific data processing tutorial
 
 .. include:: ../../includes/big_toc_css.rst
 
-.. note:: This document is meant to be used with **scikit-learn version 0.7+**.
-
 .. warning::
 
     In scikit-learn release 0.9, the import path has changed from

doc/tutorial/statistical_inference/iris_classification_exercise.rst

@@ -1,8 +0,0 @@
-Exercise: classification of iris
-====================================
-
-.. literalinclude:: ../../auto_examples/exercises/plot_iris_exercise.py
-    :lines: 1-10 
-
-Solution: :download:`../../auto_examples/exercises/plot_iris_exercise.py`
-

doc/tutorial/statistical_inference/model_selection.rst

@@ -105,12 +105,12 @@ of the computer.
 
     - Takes a label array to group observations
 
+.. currentmodule:: sklearn.svm
+
 .. image:: ../../auto_examples/exercises/images/plot_cv_digits_1.png
    :target: ../../tutorial/statistical_inference/digits_cv_exercise.html
    :align: right
-   :scale: 75
-
-.. currentmodule:: sklearn.svm
+   :scale: 100
 
 .. topic:: **Exercise**
    :class: green
@@ -119,9 +119,12 @@ of the computer.
    estimator with an RBF kernel as a function of parameter `C` (use a 
    logarithmic grid of points, from `1` to `10`).
 
-   .. toctree::
+   .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
+       :lines: 13-23 
+
+   Solution: :download:`../../auto_examples/exercises/plot_cv_digits.py`
+
 
-      digits_cv_exercise.rst
 
 Grid-search and cross-validated estimators
 ============================================
@@ -209,6 +212,9 @@ appended to their name.
 
    **Bonus**: How much can you trust the selection of alpha?
 
-   .. toctree::
+   .. literalinclude:: ../../auto_examples/exercises/plot_cv_diabetes.py
+       :lines: 11-23
+
+   Solution: :download:`../../auto_examples/exercises/plot_cv_diabetes.py`
+
 
-      diabetes_cv_exercise

doc/tutorial/statistical_inference/supervised_learning.rst

@@ -401,9 +401,11 @@ This is known as :class:`LogisticRegression`.
    model. Leave out the last 10% and test prediction performance on these
    observations.
 
-   .. toctree::
+   .. literalinclude:: ../../auto_examples/exercises/plot_digits_classification_exercise.py
+       :lines: 12-17
+
+   Solution: :download:`../../auto_examples/exercises/plot_digits_classification_exercise.py`
 
-        digits_classification_exercise
 
 Support vector machines (SVMs)
 ================================
@@ -551,27 +553,23 @@ creating an decision energy by positioning *kernels* on observations:
     :align: right
     :scale: 70
 
-.. topic:: **Excercise**
+.. topic:: **Exercise**
    :class: green
 
    Try classifying classes 1 and 2 from the iris dataset with SVMs, with
    the 2 first features. Leave out 10% of each class and test prediction
    performance on these observations.
 
-   The solution is available below:
-
-   .. toctree::
-
-        iris_classification_exercise.rst
-
    **Warning**: the classes are ordered, do not leave out the last 10%,
    you would be testing on only one class.
 
    **Hint**: You can use the `decision_function` method on a grid to get
    intuitions.
 
-..  
- Gaussian process: introducing the notion of posterior estimate
- ===============================================================
+   .. literalinclude:: ../../auto_examples/exercises/plot_iris_exercise.py
+       :lines: 15-22 
+
+   Solution: :download:`../../auto_examples/exercises/plot_iris_exercise.py`
+
 
 

examples/cluster/plot_lena_compress.py

@@ -26,7 +26,12 @@ from sklearn import cluster
 n_clusters = 5
 np.random.seed(0)
 
-lena = sp.lena()
+try:
+    lena = sp.lena()
+except AttributeError:
+    # Newer versions of scipy have lena in misc
+    from scipy import misc
+    lena = misc.lena()
 X = lena.reshape((-1, 1)) # We need an (n_sample, n_feature) array
 k_means = cluster.KMeans(k=n_clusters, n_init=4)
 k_means.fit(X)

examples/exercises/plot_cv_diabetes.py

@@ -3,12 +3,11 @@
 Cross-validation on diabetes Dataset Exercise
 ===============================================
 
-This exercise is used in the
-:ref:`cv_estimators_tut` part of the 
-:ref:`model_selection_tut` section of the
-:ref:`stat_learn_tut_index`.
+This exercise is used in the :ref:`cv_estimators_tut` part of the
+:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
 """
 print __doc__
+
 import numpy as np
 import pylab as pl
 

examples/exercises/plot_digits_classification_exercise.py

@@ -3,8 +3,7 @@
 Digits Classification Exercise
 ================================
 
-This exercise is used in the
-:ref:`clf_tut` part of the 
+This exercise is used in the :ref:`clf_tut` part of the
 :ref:`supervised_learning_tut` section of the
 :ref:`stat_learn_tut_index`.
 """