def test_multiple_init():
# Test that multiple inits does not much worse than a single one
X = rng.randn(30, 5)
X[:10] += 2
g = mixture.GMM(n_components=2, covariance_type='spherical',
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_true(train2 >= train1 - 1.e-2)
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