def _compute_covariance(self):
"""Computes the covariance matrix for each Gaussian kernel using
covariance_factor().
"""
self.factor = self.covariance_factor()
# Cache covariance and inverse covariance of the data
if not hasattr(self, '_data_inv_cov'):
self._data_covariance = atleast_2d(
cov(self.dataset, rowvar=1, bias=False, aweights=self.weights))
self._data_inv_cov = linalg.inv(self._data_covariance)
self.covariance = self._data_covariance * self.factor**2
self.inv_cov = self._data_inv_cov / self.factor**2
self._norm_factor = sqrt(linalg.det(2 * pi * self.covariance)) * self.n
def _compute_covariance(self):
"""Computes the covariance matrix for each Gaussian kernel using
covariance_factor().
"""
self.factor = self.covariance_factor()
# Cache covariance and inverse covariance of the data
if not hasattr(self, '_data_inv_cov'):
self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,
bias=False,
aweights=self.weights))
self._data_inv_cov = linalg.inv(self._data_covariance)
self.covariance = self._data_covariance * self.factor**2
self.inv_cov = self._data_inv_cov / self.factor**2
self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n