本文整理汇总了Python中sklearn.mixture.GaussianMixture.sample方法的典型用法代码示例。如果您正苦于以下问题：Python GaussianMixture.sample方法的具体用法？Python GaussianMixture.sample怎么用？Python GaussianMixture.sample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.GaussianMixture的用法示例。
在下文中一共展示了GaussianMixture.sample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: GaussianMixture1D
# 需要导入模块: from sklearn.mixture import GaussianMixture [as 别名]
# 或者: from sklearn.mixture.GaussianMixture import sample [as 别名]
class GaussianMixture1D(object):
"""
Simple class to work with 1D mixtures of Gaussians
Parameters
----------
means : array_like
means of component distributions (default = 0)
sigmas : array_like
standard deviations of component distributions (default = 1)
weights : array_like
weight of component distributions (default = 1)
"""
def __init__(self, means=0, sigmas=1, weights=1):
data = np.array([t for t in np.broadcast(means, sigmas, weights)])
components = data.shape[0]
self._gmm = GaussianMixture(components, covariance_type='spherical')
self._gmm.means_ = data[:, :1]
self._gmm.weights_ = data[:, 2] / data[:, 2].sum()
self._gmm.covariances_ = data[:, 1] ** 2
self._gmm.precisions_cholesky_ = 1 / np.sqrt(self._gmm.covariances_)
self._gmm.fit = None # disable fit method for safety
def sample(self, size):
"""Random sample"""
return self._gmm.sample(size)
def pdf(self, x):
"""Compute probability distribution"""
if x.ndim == 1:
x = x[:, np.newaxis]
logprob = self._gmm.score_samples(x)
return np.exp(logprob)
def pdf_individual(self, x):
"""Compute probability distribution of each component"""
if x.ndim == 1:
x = x[:, np.newaxis]
logprob = self._gmm.score_samples(x)
responsibilities = self._gmm.predict_proba(x)
return responsibilities * np.exp(logprob[:, np.newaxis])