本文整理汇总了Python中sklearn.mixture.gaussian_mixture.GaussianMixture.aic方法的典型用法代码示例。如果您正苦于以下问题：Python GaussianMixture.aic方法的具体用法？Python GaussianMixture.aic怎么用？Python GaussianMixture.aic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.aic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_gaussian_mixture_aic_bic
# 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 aic [as 别名]
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