hmm.py 文件源码

def _fix_priors_shape(self):
        # If priors are numbers, this function will make them into a
        # matrix of proper shape
        self.weights_prior = np.broadcast_to(
            self.weights_prior, (self.n_components, self.n_mix)).copy()
        self.means_prior = np.broadcast_to(
            self.means_prior,
            (self.n_components, self.n_mix, self.n_features)).copy()
        self.means_weight = np.broadcast_to(
            self.means_weight,
            (self.n_components, self.n_mix)).copy()

        if self.covariance_type == "full":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_mix,
                 self.n_features, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, (self.n_components, self.n_mix)).copy()
        elif self.covariance_type == "tied":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_features, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, self.n_components).copy()
        elif self.covariance_type == "diag":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_mix, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight,
                (self.n_components, self.n_mix, self.n_features)).copy()
        elif self.covariance_type == "spherical":
            self.covars_prior = np.broadcast_to(
                self.covars_prior, (self.n_components, self.n_mix)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, (self.n_components, self.n_mix)).copy()