def test_mvn_to_mvn():
means = 123.0 * np.ones((1, 1))
covs = 4.0 * np.ones((1, 1, 1))
gmm = GMM(n_components=1, priors=np.ones(1), means=means, covariances=covs)
mvn = gmm.to_mvn()
assert_array_almost_equal(mvn.mean, means[0])
assert_array_almost_equal(mvn.covariance, covs[0])
def test_2_components_to_mvn():
priors = np.array([0.25, 0.75])
means = np.array([[1.0, 2.0], [3.0, 4.0]])
covs = np.array([
[[1.0, 0.0], [0.0, 1.0]],
[[1.0, 0.0], [0.0, 1.0]],
])
gmm = GMM(n_components=1, priors=priors, means=means, covariances=covs)
mvn = gmm.to_mvn()
assert_array_almost_equal(mvn.mean, np.array([2.5, 3.5]))
def test_gmm_to_mvn_vs_mvn():
random_state = check_random_state(0)
gmm = GMM(n_components=2, random_state=random_state)
gmm.from_samples(X)
mvn_from_gmm = gmm.to_mvn()
mvn = MVN(random_state=random_state)
mvn.from_samples(X)
assert_array_almost_equal(mvn_from_gmm.mean, mvn.mean)
assert_array_almost_equal(mvn_from_gmm.covariance,
mvn.covariance,
decimal=3)