Pushing the docs for revision for branch: master, commit 8f199fe4f59e4a7d76e8e764cdc231071b84f974

Author: sklearn-ci

dev/_downloads/plot_gmm_covariances.py

@@ -47,14 +47,14 @@
 def make_ellipses(gmm, ax):
     for n, color in enumerate(colors):
         if gmm.covariance_type == 'full':
-            covars = gmm.covariances_[n][:2, :2]
+            covariances = gmm.covariances_[n][:2, :2]
         elif gmm.covariance_type == 'tied':
-            covars = gmm.covariances_[:2, :2]
+            covariances = gmm.covariances_[:2, :2]
         elif gmm.covariance_type == 'diag':
-            covars = np.diag(gmm.covariances_[n][:2])
+            covariances = np.diag(gmm.covariances_[n][:2])
         elif gmm.covariance_type == 'spherical':
-            covars = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]
-        v, w = np.linalg.eigh(covars)
+            covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]
+        v, w = np.linalg.eigh(covariances)
         u = w[0] / np.linalg.norm(w[0])
         angle = np.arctan2(u[1], u[0])
         angle = 180 * angle / np.pi  # convert to degrees
@@ -82,9 +82,9 @@ def make_ellipses(gmm, ax):
 n_classes = len(np.unique(y_train))
 
 # Try GMMs using different types of covariances.
-estimators = dict((covar_type, GaussianMixture(n_components=n_classes,
-                   covariance_type=covar_type, max_iter=20))
-                  for covar_type in ['spherical', 'diag', 'tied', 'full'])
+estimators = dict((cov_type, GaussianMixture(n_components=n_classes,
+                   covariance_type=cov_type, max_iter=20, random_state=0))
+                  for cov_type in ['spherical', 'diag', 'tied', 'full'])
 
 n_estimators = len(estimators)
 

dev/_downloads/plot_gmm_selection.py

@@ -75,9 +75,9 @@
 # Plot the winner
 splot = plt.subplot(2, 1, 2)
 Y_ = clf.predict(X)
-for i, (mean, covar, color) in enumerate(zip(clf.means_, clf.covariances_,
-                                             color_iter)):
-    v, w = linalg.eigh(covar)
+for i, (mean, cov, color) in enumerate(zip(clf.means_, clf.covariances_,
+                                           color_iter)):
+    v, w = linalg.eigh(cov)
     if not np.any(Y_ == i):
         continue
     plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color)