本文整理汇总了Python中sklearn.mixture.gaussian_mixture.GaussianMixture.precisions_init方法的典型用法代码示例。如果您正苦于以下问题：Python GaussianMixture.precisions_init方法的具体用法？Python GaussianMixture.precisions_init怎么用？Python GaussianMixture.precisions_init使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.mixture.gaussian_mixture.GaussianMixture的用法示例。
在下文中一共展示了GaussianMixture.precisions_init方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_check_precisions
# 需要导入模块: from sklearn.mixture.gaussian_mixture import GaussianMixture [as 别名]
# 或者: from sklearn.mixture.gaussian_mixture.GaussianMixture import precisions_init [as 别名]
def test_check_precisions():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
n_components, n_features = rand_data.n_components, rand_data.n_features
# Define the bad precisions for each covariance_type
precisions_bad_shape = {
'full': np.ones((n_components + 1, n_features, n_features)),
'tied': np.ones((n_features + 1, n_features + 1)),
'diag': np.ones((n_components + 1, n_features)),
'spherical': np.ones((n_components + 1))}
# Define not positive-definite precisions
precisions_not_pos = np.ones((n_components, n_features, n_features))
precisions_not_pos[0] = np.eye(n_features)
precisions_not_pos[0, 0, 0] = -1.
precisions_not_positive = {
'full': precisions_not_pos,
'tied': precisions_not_pos[0],
'diag': np.full((n_components, n_features), -1.),
'spherical': np.full(n_components, -1.)}
not_positive_errors = {
'full': 'symmetric, positive-definite',
'tied': 'symmetric, positive-definite',
'diag': 'positive',
'spherical': 'positive'}
for covar_type in COVARIANCE_TYPE:
X = RandomData(rng).X[covar_type]
g = GaussianMixture(n_components=n_components,
covariance_type=covar_type,
random_state=rng)
# Check precisions with bad shapes
g.precisions_init = precisions_bad_shape[covar_type]
assert_raise_message(ValueError,
"The parameter '%s precision' should have "
"the shape of" % covar_type,
g.fit, X)
# Check not positive precisions
g.precisions_init = precisions_not_positive[covar_type]
assert_raise_message(ValueError,
"'%s precision' should be %s"
% (covar_type, not_positive_errors[covar_type]),
g.fit, X)
# Check the correct init of precisions_init
g.precisions_init = rand_data.precisions[covar_type]
g.fit(X)
assert_array_equal(rand_data.precisions[covar_type], g.precisions_init)