def test_gaussian_mixture_aic_bic():
# Test the aic and bic criteria
rng = np.random.RandomState(0)
n_samples, n_features, n_components = 50, 3, 2
X = rng.randn(n_samples, n_features)
# standard gaussian entropy
sgh = 0.5 * (fast_logdet(np.cov(X.T, bias=1)) + n_features *
(1 + np.log(2 * np.pi)))
for cv_type in COVARIANCE_TYPE:
g = GaussianMixture(n_components=n_components,
covariance_type=cv_type,
random_state=rng,
max_iter=200)
g.fit(X)
aic = 2 * n_samples * sgh + 2 * g._n_parameters()
bic = (2 * n_samples * sgh + np.log(n_samples) * g._n_parameters())
bound = n_features / np.sqrt(n_samples)
assert (g.aic(X) - aic) / n_samples < bound
assert (g.bic(X) - bic) / n_samples < bound
def test_gaussian_mixture_n_parameters():
# Test that the right number of parameters is estimated
rng = np.random.RandomState(0)
n_samples, n_features, n_components = 50, 5, 2
X = rng.randn(n_samples, n_features)
n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41}
for cv_type in COVARIANCE_TYPE:
g = GaussianMixture(n_components=n_components,
covariance_type=cv_type,
random_state=rng).fit(X)
assert g._n_parameters() == n_params[cv_type]