Use n_components=3 to test actual regression

n_components and n_features were equal and one was used for the other in
some places.

sklearn/mixture/tests/test_gaussian_mixture.py

@@ -918,7 +918,7 @@ def test_property():
 
 def test_sample():
     rng = np.random.RandomState(0)
-    rand_data = RandomData(rng, scale=7)
+    rand_data = RandomData(rng, scale=7, n_components=3)
     n_features, n_components = rand_data.n_features, rand_data.n_components
 
     for covar_type in COVARIANCE_TYPE:
@@ -937,7 +937,8 @@ def test_sample():
         # Just to make sure the class samples correctly
         n_samples = 20000
         X_s, y_s = gmm.sample(n_samples)
-        for k in range(n_features):
+
+        for k in range(n_components):
             if covar_type == 'full':
                 assert_array_almost_equal(gmm.covariances_[k],
                                           np.cov(X_s[y_s == k].T), decimal=1)
@@ -954,15 +955,16 @@ def test_sample():
                     decimal=1)
 
         means_s = np.array([np.mean(X_s[y_s == k], 0)
-                           for k in range(n_features)])
+                           for k in range(n_components)])
         assert_array_almost_equal(gmm.means_, means_s, decimal=1)
 
-        # Check that sizes that are drawn match what is requested
-        assert_equal(X_s.shape, (n_samples, n_components))
-        for sample_size in range(1, 50):
-            X_s, _ = gmm.sample(sample_size)
-            assert_equal(X_s.shape, (sample_size, n_components))
+        # Check shapes of sampled data, see
+        # https://github.com/scikit-learn/scikit-learn/issues/7701
+        assert_equal(X_s.shape, (n_samples, n_features))
 
+        for sample_size in range(1, 100):
+            X_s, _ = gmm.sample(sample_size)
+            assert_equal(X_s.shape, (sample_size, n_features))
 
 
 @ignore_warnings(category=ConvergenceWarning)