modified test_gmm to match API changes in gmm.py

sklearn/mixture/tests/test_gmm.py

@@ -191,17 +191,23 @@ class GMMTester():
         X = g.sample(n_samples=100)
         g = self.model(n_components=self.n_components,
                        covariance_type=self.covariance_type,
-                       random_state=rng, min_covar=1e-1)
-        g.fit(X, n_iter=1, init_params=params)
+                       random_state=rng, min_covar=1e-1,
+                       n_iter=1, init_params=params)
+        g.fit(X)
 
         # Do one training iteration at a time so we can keep track of
         # the log likelihood to make sure that it increases after each
         # iteration.
         trainll = []
         for iter in xrange(5):
-            g.fit(X, n_iter=1, params=params, init_params='')
+            g.params = params
+            g.init_params = ''
+            g.fit(X)
             trainll.append(self.score(g, X))
-        g.fit(X, n_iter=10, params=params, init_params='')  # finish fitting
+        g.n_iter = 10
+        g.init_params = ''
+        g.params = params
+        g.fit(X)  # finish fitting
 
         # Note that the log likelihood will sometimes decrease by a
         # very small amount after it has more or less converged due to
@@ -222,8 +228,9 @@ class GMMTester():
         X = rng.randn(100, self.n_features)
         X.T[1:] = 0
         g = self.model(n_components=2, covariance_type=self.covariance_type,
-                       random_state=rng, min_covar=1e-3)
-        g.fit(X, n_iter=5, init_params=params)
+                       random_state=rng, min_covar=1e-3, n_iter=5, 
+                       init_params=params)
+        g.fit(X)
         trainll = g.score(X)
         self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5)
 
@@ -234,8 +241,9 @@ class GMMTester():
         X = rng.randn(100, 1)
         #X.T[1:] = 0
         g = self.model(n_components=2, covariance_type=self.covariance_type,
-                       random_state=rng, min_covar=1e-7)
-        g.fit(X, n_iter=5, init_params=params)
+                       random_state=rng, min_covar=1e-7, n_iter=5, 
+                       init_params=params)
+        g.fit(X)
         trainll = g.score(X)
         if isinstance(g, mixture.DPGMM):
             self.assertTrue(np.sum(np.abs(trainll / 100)) < 5)
@@ -271,9 +279,10 @@ def test_multiple_init():
     X = rng.randn(30, 5)
     X[:10] += 2
     g = mixture.GMM(n_components=2, covariance_type='spherical',
-                    random_state=rng, min_covar=1e-7)
-    train2 = g.fit(X, n_iter=5, n_init=5).score(X).sum()
-    train1 = g.fit(X, n_iter=5).score(X).sum()
+                    random_state=rng, min_covar=1e-7, n_iter=5)
+    train1 = g.fit(X).score(X).sum()
+    g.n_init = 5
+    train2 = g.fit(X).score(X).sum()
     assert train2 >= train1 - 1.e-2
 
 
@@ -284,8 +293,8 @@ def test_n_parameters():
     n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41}
     for cv_type in ['full', 'tied', 'diag', 'spherical']:
         g = mixture.GMM(n_components=n_components, covariance_type=cv_type,
-                        random_state=rng, min_covar=1e-7)
-        g.fit(X, n_iter=1)
+                        random_state=rng, min_covar=1e-7, n_iter=1)
+        g.fit(X)
         assert g._n_parameters() == n_params[cv_type]