def test_compare_covar_type():
# We can compare the 'full' precision with the other cov_type if we apply
# 1 iter of the M-step (done during _initialize_parameters).
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
X = rand_data.X['full']
n_components = rand_data.n_components
for prior_type in PRIOR_TYPE:
# Computation of the full_covariance
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='full',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
full_covariances = (
bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis, np.newaxis])
# Check tied_covariance = mean(full_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='tied',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
tied_covariance = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(tied_covariance, np.mean(full_covariances, 0))
# Check diag_covariance = diag(full_covariances)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='diag',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
diag_covariances = (bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis])
assert_almost_equal(diag_covariances,
np.array([np.diag(cov)
for cov in full_covariances]))
# Check spherical_covariance = np.mean(diag_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='spherical',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
spherical_covariances = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(
spherical_covariances, np.mean(diag_covariances, 1))
def test_compare_covar_type():
# We can compare the 'full' precision with the other cov_type if we apply
# 1 iter of the M-step (done during _initialize_parameters).
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
X = rand_data.X['full']
n_components = rand_data.n_components
for prior_type in PRIOR_TYPE:
# Computation of the full_covariance
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='full',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
full_covariances = (
bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis, np.newaxis])
# Check tied_covariance = mean(full_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='tied',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
tied_covariance = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(tied_covariance, np.mean(full_covariances, 0))
# Check diag_covariance = diag(full_covariances)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='diag',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
diag_covariances = (bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis])
assert_almost_equal(diag_covariances,
np.array([np.diag(cov)
for cov in full_covariances]))
# Check spherical_covariance = np.mean(diag_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='spherical',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
spherical_covariances = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(
spherical_covariances, np.mean(diag_covariances, 1))
