DOC: fogot doctests in python files.

Author: amueller

sklearn/svm/classes.py

@@ -150,8 +150,8 @@ class SVC(DenseBaseLibSVM, ClassifierMixin):
     >>> from sklearn.svm import SVC
     >>> clf = SVC()
     >>> clf.fit(X, y)
-    SVC(C=1.0, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', probability=False,
-      shrinking=True, tol=0.001)
+    SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf',
+      probability=False, shrinking=True, tol=0.001)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
 
@@ -254,8 +254,8 @@ class NuSVC(DenseBaseLibSVM, ClassifierMixin):
     >>> from sklearn.svm import NuSVC
     >>> clf = NuSVC()
     >>> clf.fit(X, y)
-    NuSVC(coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5, probability=False,
-       shrinking=True, tol=0.001)
+    NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5,
+       probability=False, shrinking=True, tol=0.001)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
 
@@ -346,8 +346,8 @@ class SVR(DenseBaseLibSVM, RegressorMixin):
     >>> X = np.random.randn(n_samples, n_features)
     >>> clf = SVR(C=1.0, epsilon=0.2)
     >>> clf.fit(X, y)
-    SVR(C=1.0, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2, kernel='rbf',
-      probability=False, shrinking=True, tol=0.001)
+    SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2,
+      kernel='rbf', probability=False, shrinking=True, tol=0.001)
 
     See also
     --------
@@ -461,8 +461,8 @@ class NuSVR(DenseBaseLibSVM, RegressorMixin):
     >>> X = np.random.randn(n_samples, n_features)
     >>> clf = NuSVR(C=1.0, nu=0.1)
     >>> clf.fit(X, y)
-    NuSVR(C=1.0, coef0=0.0, degree=3, gamma=0.2, kernel='rbf', nu=0.1,
-       probability=False, shrinking=True, tol=0.001)
+    NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.2, kernel='rbf',
+       nu=0.1, probability=False, shrinking=True, tol=0.001)
 
     See also
     --------

sklearn/svm/sparse/classes.py

@@ -23,8 +23,8 @@ class SVC(SparseBaseLibSVM, ClassifierMixin):
     >>> from sklearn.svm.sparse import SVC
     >>> clf = SVC()
     >>> clf.fit(X, y)
-    SVC(C=1.0, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', probability=False,
-      shrinking=True, tol=0.001)
+    SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf',
+      probability=False, shrinking=True, tol=0.001)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
     """
@@ -57,8 +57,8 @@ class NuSVC(SparseBaseLibSVM, ClassifierMixin):
     >>> from sklearn.svm.sparse import NuSVC
     >>> clf = NuSVC()
     >>> clf.fit(X, y)
-    NuSVC(coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5, probability=False,
-       shrinking=True, tol=0.001)
+    NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5,
+       probability=False, shrinking=True, tol=0.001)
     >>> print clf.predict([[-0.8, -1]])
     [ 1.]
     """
@@ -93,8 +93,8 @@ class SVR(SparseBaseLibSVM, RegressorMixin):
     >>> X = np.random.randn(n_samples, n_features)
     >>> clf = SVR(C=1.0, epsilon=0.2)
     >>> clf.fit(X, y)
-    SVR(C=1.0, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2, kernel='rbf', nu=0.5,
-      probability=False, shrinking=True, tol=0.001)
+    SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2,
+      kernel='rbf', nu=0.5, probability=False, shrinking=True, tol=0.001)
     """
 
     def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
@@ -127,8 +127,8 @@ class NuSVR(SparseBaseLibSVM, RegressorMixin):
     >>> X = np.random.randn(n_samples, n_features)
     >>> clf = NuSVR(nu=0.1, C=1.0)
     >>> clf.fit(X, y)
-    NuSVR(C=1.0, coef0=0.0, degree=3, epsilon=0.1, gamma=0.2, kernel='rbf',
-       nu=0.1, probability=False, shrinking=True, tol=0.001)
+    NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.2,
+       kernel='rbf', nu=0.1, probability=False, shrinking=True, tol=0.001)
     """
 
     def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3,