COSMIT pep8

examples/svm/plot_svm_scale_c.py

@@ -101,8 +101,8 @@ n_samples = 100
 n_features = 300
 
 # L1 data (only 5 informative features)
-X_1, y_1 = datasets.make_classification(n_samples=n_samples, n_features=n_features,
-        n_informative=5, random_state=1)
+X_1, y_1 = datasets.make_classification(n_samples=n_samples,
+           n_features=n_features, n_informative=5, random_state=1)
 
 # L2 data: non sparse, but less features
 y_2 = np.sign(.5 - rnd.rand(n_samples))
@@ -148,4 +148,3 @@ for fignum, (clf, cs, X, y) in enumerate(clf_sets):
 
     pl.legend(loc="best")
 pl.show()
-