Merge branch 'fabianp/scikits'

* fabianp/scikits:
  some more changes
  some exercise changes
  more cosmetic
  cosmetic stuff
  minor changes
  cosmetic changes
  minor changes
  Small fixes.
  Initial import of scikit-learn tutorial.

Conflicts:
	advanced/index.rst

The chapter on scikits-lear comes last.

advanced/index.rst

@@ -51,6 +51,4 @@ Advanced topics
    sympy.rst
    3d_plotting/index.rst
    image_processing/index.rst
-
-
-=======
+   scikit-learn/index.rst

advanced/scikit-learn/digits_svm.py

@@ -0,0 +1,12 @@
+from scikits.learn import datasets, svm
+import pylab as pl
+
+digits = datasets.load_digits()
+clf = svm.LinearSVC(fit_intercept=False)
+clf.fit(digits.data, digits.target)
+
+for i in range(4):
+    pl.subplot(2, 4, 1 + i)
+    pl.imshow(clf.coef_[i].reshape(8, 8), cmap=pl.cm.gray_r, interpolation='nearest')
+    pl.axis('off')
+pl.show()

advanced/scikit-learn/faces.py

@@ -0,0 +1,35 @@
+"""
+Stripped-down version of the face recognition example by Olivier Grisel
+
+http://scikit-learn.sourceforge.net/dev/auto_examples/applications/face_recognition.html
+
+## original shape of images: 50, 37
+"""
+
+import numpy as np
+from scikits.learn import cross_val, datasets, decomposition, svm
+
+# ..
+# .. load data ..
+lfw_people = datasets.fetch_lfw_people(min_faces_per_person=70, resize=0.4)
+faces = np.reshape(lfw_people.data, (lfw_people.target.shape[0], -1))
+train, test = iter(cross_val.StratifiedKFold(lfw_people.target, k=4)).next()
+X_train, X_test = faces[train], faces[test]
+y_train, y_test = lfw_people.target[train], lfw_people.target[test]
+
+# ..
+# .. dimension reduction ..
+pca = decomposition.RandomizedPCA(n_components=150, whiten=True)
+pca.fit(X_train)
+X_train_pca = pca.transform(X_train)
+X_test_pca = pca.transform(X_test)
+
+# ..
+# .. classification ..
+clf = svm.SVC(C=5., gamma=0.001)
+clf.fit(X_train_pca, y_train)
+
+print 'Score on unseen data: '
+print clf.score(X_test_pca, y_test)
+
+