GMM.py 文件源码

def _step_E(self, points):
        """
        In this step the algorithm evaluates the responsibilities of each points in each cluster

        Parameters
        ----------
        points : an array (n_points,dim)

        Returns
        -------
        log_resp: an array (n_points,n_components)
            an array containing the logarithm of the responsibilities.
        log_prob_norm : an array (n_points,)
            logarithm of the probability of each sample in points

        """
        log_normal_matrix = _log_normal_matrix(points,self.means,self.cov,self.covariance_type,self.n_jobs)
        log_product = log_normal_matrix + self.log_weights[:,np.newaxis].T
        log_prob_norm = logsumexp(log_product,axis=1)

        log_resp = log_product - log_prob_norm[:,np.newaxis]

        return log_prob_norm,log_resp