use scipy.linalg in test_nmf.py

Author: agramfort

sklearn/decomposition/tests/test_nmf.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy import linalg
 from sklearn.decomposition import nmf
 
 from sklearn.utils.testing import assert_true
@@ -33,8 +34,8 @@ def test_initialize_close():
     """
     A = np.abs(random_state.randn(10, 10))
     W, H = nmf._initialize_nmf(A, 10)
-    error = np.linalg.norm(np.dot(W, H) - A)
-    sdev = np.linalg.norm(A - A.mean())
+    error = linalg.norm(np.dot(W, H) - A)
+    sdev = linalg.norm(A - A.mean())
     assert_true(error <= sdev)
 
 
@@ -147,7 +148,7 @@ def test_sparse_input():
                                       random_state=999)
     T2 = pg_nmf.fit_transform(A_sparse)
     assert_array_almost_equal(pg_nmf.reconstruction_err_,
-                  np.linalg.norm(A - np.dot(T2, pg_nmf.components_), 'fro'))
+                  linalg.norm(A - np.dot(T2, pg_nmf.components_), 'fro'))
     assert_array_almost_equal(T1, T2)
 
     # same with sparseness