Merge pull request scikit-learn#1470 from kuantkid/fix_spectral_cluster_test

FIX spectral clustering test error in OSX and ADD spectral embedding to sphere examples

Author: ogrisel

examples/manifold/plot_manifold_sphere.py

@@ -124,4 +124,19 @@
 ax.yaxis.set_major_formatter(NullFormatter())
 pl.axis('tight')
 
+# Perform Spectral Embedding.
+t0 = time()
+se = manifold.SpectralEmbedding(n_components=2,
+                                n_neighbors=n_neighbors)
+trans_data = se.fit_transform(sphere_data).T
+t1 = time()
+print "Spectral Embedding: %.2g sec" % (t1 - t0)
+
+ax = fig.add_subplot(248)
+pl.scatter(trans_data[0], trans_data[1],  c=colors, cmap=pl.cm.rainbow)
+pl.title("Spectral Embedding (%.2g sec)" % (t1 - t0))
+ax.xaxis.set_major_formatter(NullFormatter())
+ax.yaxis.set_major_formatter(NullFormatter())
+pl.axis('tight')
+
 pl.show()

sklearn/cluster/tests/test_spectral.py

@@ -18,14 +18,13 @@
 
 
 def test_spectral_clustering():
-    S = np.array([[1, 5, 2, 1, 0, 0, 0],
-                  [5, 1, 3, 1, 0, 0, 0],
-                  [2, 3, 1, 1, 0, 0, 0],
-                  [1, 1, 1, 1, 2, 1, 1],
-                  [0, 0, 0, 2, 2, 3, 2],
-                  [0, 0, 0, 1, 3, 1, 4],
-                  [0, 0, 0, 1, 2, 4, 1],
-                  ])
+    S = np.array([[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+                  [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+                  [1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
+                  [0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
+                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
+                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
+                  [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]])
 
     for eigen_solver in ('arpack', 'lobpcg'):
         for assign_labels in ('kmeans', 'discretize'):
@@ -155,7 +154,7 @@ def test_spectral_clustering_sparse():
 
 
 def test_affinities():
-    X, y = make_blobs(n_samples=40, random_state=1, centers=[[1, 1], [-1, -1]],
+    X, y = make_blobs(n_samples=40, random_state=2, centers=[[1, 1], [-1, -1]],
                       cluster_std=0.4)
     # nearest neighbors affinity
     sp = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',