scikit-learn
diff --git a/‎doc/modules/sgd.rst
Copy file name to clipboardExpand all lines: doc/modules/sgd.rst
+1-1Lines changed: 1 addition & 1 deletion b/‎doc/modules/sgd.rst
Copy file name to clipboardExpand all lines: doc/modules/sgd.rst
+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎examples/linear_model/plot_sgd_comparison.py
Copy file name to clipboardExpand all lines: examples/linear_model/plot_sgd_comparison.py
+2-2Lines changed: 2 additions & 2 deletions b/‎examples/linear_model/plot_sgd_comparison.py
Copy file name to clipboardExpand all lines: examples/linear_model/plot_sgd_comparison.py
+2-2Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/feature_selection/tests/test_selector_mixin.py
Copy file name to clipboardExpand all lines: sklearn/feature_selection/tests/test_selector_mixin.py
+2-2Lines changed: 2 additions & 2 deletions b/‎sklearn/feature_selection/tests/test_selector_mixin.py
Copy file name to clipboardExpand all lines: sklearn/feature_selection/tests/test_selector_mixin.py
+2-2Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/linear_model/stochastic_gradient.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/stochastic_gradient.py
+6-5Lines changed: 6 additions & 5 deletions b/‎sklearn/linear_model/stochastic_gradient.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/stochastic_gradient.py
+6-5Lines changed: 6 additions & 5 deletions
diff --git a/‎sklearn/linear_model/tests/test_perceptron.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/tests/test_perceptron.py
+1-1Lines changed: 1 addition & 1 deletion b/‎sklearn/linear_model/tests/test_perceptron.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/tests/test_perceptron.py
+1-1Lines changed: 1 addition & 1 deletion
@@ -392,7 +392,7 @@ For PA-1 use ``loss='pa1'``, and ``loss='pa2'`` for
 PA-2 respectively. Setting ``C`` to 0 gives the vanilla passive-
 aggressive algorithm.
 
-..math::
+.. math::
 
      \eta = \frac{1}{|x|^2} (PA)
      \eta = \text{min} (\alpha, \frac{1}{|x|^2} (PA-1)
 
@@ -1,7 +1,7 @@
 """
-================================
+=============================================
 Comparing various Stochastic Gradient Solvers
-================================
+=============================================
 
 An example showing how the different SGDClassifiers perform
 on the hand-written digits dataset
 
@@ -16,7 +16,7 @@
 def test_transform_linear_model():
     for clf in (LogisticRegression(C=0.1),
                 LinearSVC(C=0.01, dual=False),
-                SGDClassifier(alpha=0.1, n_iter=10, shuffle=True, seed=0)):
+                SGDClassifier(alpha=0.1, n_iter=10, shuffle=True, random_state=0)):
         for thresh in (None, ".09*mean", "1e-5 * median"):
             for func in (np.array, sp.csr_matrix):
                 X = func(iris.data)
@@ -34,7 +34,7 @@ def test_transform_linear_model():
 
 
 def test_invalid_input():
-    clf = SGDClassifier(alpha=0.1, n_iter=10, shuffle=True, seed=0)
+    clf = SGDClassifier(alpha=0.1, n_iter=10, shuffle=True, random_state=None)
 
     clf.fit(iris.data, iris.target)
     assert_raises(ValueError, clf.transform, iris.data, "gobbledigook")
 
@@ -344,10 +344,11 @@ class SGDClassifier(BaseSGD, LinearClassifierMixin, SelectorMixin):
     >>> clf = linear_model.SGDClassifier()
     >>> clf.fit(X, Y)
     ... #doctest: +NORMALIZE_WHITESPACE
-    SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
+    SGDClassifier(C=1.0, alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,
             fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',
             loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,
-            rho=None, random_state=0, shuffle=False, verbose=0, warm_start=False)
+            random_state=0, rho=None, shuffle=False, verbose=0,
+            warm_start=False)
     >>> print(clf.predict([[-0.8, -1]]))
     [1]
 
@@ -773,10 +774,10 @@ class SGDRegressor(BaseSGD, RegressorMixin, SelectorMixin):
     >>> X = np.random.randn(n_samples, n_features)
     >>> clf = linear_model.SGDRegressor()
     >>> clf.fit(X, y)
-    SGDRegressor(alpha=0.0001, epsilon=0.1, eta0=0.01, fit_intercept=True,
+    SGDRegressor(C=1.0, alpha=0.0001, epsilon=0.1, eta0=0.01, fit_intercept=True,
            l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss',
-           n_iter=5, p=None, penalty='l2', power_t=0.25, rho=None, random_state=0,
-           shuffle=False, verbose=0, warm_start=False)
+           n_iter=5, p=None, penalty='l2', power_t=0.25, random_state=0,
+           rho=None, shuffle=False, verbose=0, warm_start=False)
 
     See also
     --------
 
@@ -44,7 +44,7 @@ def predict(self, X):
 
 def test_perceptron_accuracy():
     for data in (X, X_csr):
-        clf = Perceptron(n_iter=30, shuffle=False, seed=0)
+        clf = Perceptron(n_iter=30, shuffle=False, random_state=None)
         clf.fit(data, y)
         score = clf.score(data, y)
         assert_true(score >= 0.7)