'Computes the covariance matrix for each Gaussian kernel using
covariance_factor().'

def _compute_covariance(self):

    self.factor = self.covariance_factor()
    if (not hasattr(self, '_data_inv_cov')):
        self._data_covariance = atleast_2d(np.cov(self.dataset, rowvar=1, bias=False))
        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)
