def test_regularisation():
# We train the GaussianMixture on degenerate data by defining two clusters
# of a 0 covariance.
rng = np.random.RandomState(0)
n_samples, n_features = 10, 5
X = np.vstack((np.ones(
(n_samples // 2, n_features)), np.zeros((n_samples // 2, n_features))))
for covar_type in COVARIANCE_TYPE:
gmm = GaussianMixture(n_components=n_samples,
reg_covar=0,
covariance_type=covar_type,
random_state=rng)
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
assert_raise_message(
ValueError, "Fitting the mixture model failed because "
"some components have ill-defined empirical "
"covariance (for instance caused by "
"singleton or collapsed samples). Try to "
"decrease the number of components, or "
"increase reg_covar.", gmm.fit, X)
gmm.set_params(reg_covar=1e-6).fit(X)
def test_regularisation():
# We train the GaussianMixture on degenerate data by defining two clusters
# of a 0 covariance.
rng = np.random.RandomState(0)
n_samples, n_features = 10, 5
X = np.vstack((np.ones((n_samples // 2, n_features)),
np.zeros((n_samples // 2, n_features))))
for covar_type in COVARIANCE_TYPE:
gmm = GaussianMixture(n_components=n_samples, reg_covar=0,
covariance_type=covar_type, random_state=rng)
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
assert_raise_message(ValueError,
"The algorithm has diverged because of too "
"few samples per components. "
"Try to decrease the number of components, "
"or increase reg_covar.", gmm.fit, X)
gmm.set_params(reg_covar=1e-6).fit(X)
def test_regularisation():
# We train the GaussianMixture on degenerate data by defining two clusters
# of a 0 covariance.
rng = np.random.RandomState(0)
n_samples, n_features = 10, 5
X = np.vstack((np.ones((n_samples // 2, n_features)),
np.zeros((n_samples // 2, n_features))))
for covar_type in COVARIANCE_TYPE:
gmm = GaussianMixture(n_components=n_samples, reg_covar=0,
covariance_type=covar_type, random_state=rng)
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
assert_raise_message(ValueError,
"Fitting the mixture model failed because "
"some components have ill-defined empirical "
"covariance (for instance caused by "
"singleton or collapsed samples). Try to "
"decrease the number of components, or "
"increase reg_covar.", gmm.fit, X)
gmm.set_params(reg_covar=1e-6).fit(X)
