def test_n_parameters():
# Test that the right number of parameters is estimated
n_samples, n_dim, n_components = 7, 5, 2
X = rng.randn(n_samples, n_dim)
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, n_iter=1)
g.fit(X)
assert_true(g._n_parameters() == n_params[cv_type])
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