diff --git a/‎examples/manifold/plot_compare_methods.py
Lines changed: 1 addition & 1 deletion b/‎examples/manifold/plot_compare_methods.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎examples/manifold/plot_manifold_sphere.py
Lines changed: 5 additions & 3 deletions b/‎examples/manifold/plot_manifold_sphere.py
Lines changed: 5 additions & 3 deletions
diff --git a/‎examples/miscellaneous/plot_kernel_approximation.py
Lines changed: 10 additions & 4 deletions b/‎examples/miscellaneous/plot_kernel_approximation.py
Lines changed: 10 additions & 4 deletions
@@ -182,7 +182,7 @@ def add_2d_scatter(ax, points, points_color, title=None):
 # Read more in the :ref:`User Guide <spectral_embedding>`.
 
 spectral = manifold.SpectralEmbedding(
-    n_components=n_components, n_neighbors=n_neighbors
+    n_components=n_components, n_neighbors=n_neighbors, random_state=42
 )
 S_spectral = spectral.fit_transform(S_points)
 
 
@@ -78,7 +78,7 @@
     t0 = time()
     trans_data = (
         manifold.LocallyLinearEmbedding(
-            n_neighbors=n_neighbors, n_components=2, method=method
+            n_neighbors=n_neighbors, n_components=2, method=method, random_state=42
         )
         .fit_transform(sphere_data)
         .T
@@ -112,7 +112,7 @@
 
 # Perform Multi-dimensional scaling.
 t0 = time()
-mds = manifold.MDS(2, max_iter=100, n_init=1, normalized_stress="auto")
+mds = manifold.MDS(2, max_iter=100, n_init=1, normalized_stress="auto", random_state=42)
 trans_data = mds.fit_transform(sphere_data).T
 t1 = time()
 print("MDS: %.2g sec" % (t1 - t0))
@@ -126,7 +126,9 @@
 
 # Perform Spectral Embedding.
 t0 = time()
-se = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors)
+se = manifold.SpectralEmbedding(
+    n_components=2, n_neighbors=n_neighbors, random_state=42
+)
 trans_data = se.fit_transform(sphere_data).T
 t1 = time()
 print("Spectral Embedding: %.2g sec" % (t1 - t0))
 
@@ -72,18 +72,24 @@
 
 # Create a classifier: a support vector classifier
 kernel_svm = svm.SVC(gamma=0.2)
-linear_svm = svm.LinearSVC(dual="auto")
+linear_svm = svm.LinearSVC(dual="auto", random_state=42)
 
 # create pipeline from kernel approximation
 # and linear svm
 feature_map_fourier = RBFSampler(gamma=0.2, random_state=1)
 feature_map_nystroem = Nystroem(gamma=0.2, random_state=1)
 fourier_approx_svm = pipeline.Pipeline(
-    [("feature_map", feature_map_fourier), ("svm", svm.LinearSVC(dual="auto"))]
+    [
+        ("feature_map", feature_map_fourier),
+        ("svm", svm.LinearSVC(dual="auto", random_state=42)),
+    ]
 )
 
 nystroem_approx_svm = pipeline.Pipeline(
-    [("feature_map", feature_map_nystroem), ("svm", svm.LinearSVC(dual="auto"))]
+    [
+        ("feature_map", feature_map_nystroem),
+        ("svm", svm.LinearSVC(dual="auto", random_state=42)),
+    ]
 )
 
 # fit and predict using linear and kernel svm:
@@ -192,7 +198,7 @@
 
 # visualize the decision surface, projected down to the first
 # two principal components of the dataset
-pca = PCA(n_components=8).fit(data_train)
+pca = PCA(n_components=8, random_state=42).fit(data_train)
 
 X = pca.transform(data_train)