Cannot easily ensure non-uniqueness without the fix, just test uniqueness

Author: vene

sklearn/utils/tests/test_extmath.py

@@ -16,7 +16,6 @@
 from sklearn.utils.extmath import density
 from sklearn.utils.extmath import logsumexp
 from sklearn.utils.extmath import randomized_svd
-from sklearn.utils.extmath import svd_flip
 from sklearn.utils.extmath import weighted_mode
 from sklearn.utils.extmath import cartesian
 from sklearn.datasets.samples_generator import make_low_rank_matrix
@@ -244,13 +243,8 @@ def test_cartesian():
 
 def test_randomized_svd_sign_flip():
     a = np.array([[2.0, 0.0], [0.0, 1.0]])
-    mismatch = False  # At least one pair should lead to mismatch
     u1, s1, v1 = randomized_svd(a, 2, flip_sign=True, random_state=41)
-    for seed in xrange(100):
-        u2, s2, v2 = randomized_svd(a, 2, flip_sign=False, random_state=seed)
-        if np.any(np.sign(u1) != np.sign(u2)):
-            mismatch = True
-        u2, s2, v2 = svd_flip(u2, s2, v2)
+    for seed in xrange(10):
+        u2, s2, v2 = randomized_svd(a, 2, flip_sign=True, random_state=seed)
         assert_almost_equal(u1, u2)
         assert_almost_equal(v1, v2)
-    assert_true(mismatch)