parameters: (self, X, log_resp)
docstring: M step.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          log_resp : array-like, shape (n_samples, n_components)
              Logarithm of the posterior probabilities (or responsibilities) of
              the point of each sample in X.
docstring_summary: M step.
docstring_tokens: M step .
function: def _m_step(self, X, log_resp):
          """M step.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          log_resp : array-like, shape (n_samples, n_components)
              Logarithm of the posterior probabilities (or responsibilities) of
              the point of each sample in X.
          """
          n_samples, _ = X.shape
          self.weights_, self.means_, self.covariances_ = (
              _estimate_gaussian_parameters(X, np.exp(log_resp), self.reg_covar,
                                            self.covariance_type))
          self.weights_ /= n_samples
          self.precisions_cholesky_ = _compute_precision_cholesky(
              self.covariances_, self.covariance_type)
function_tokens: def _m_step ( self , X , log_resp ) : n_samples , _ = X . shape self . weights_ , self . means_ , self . covariances_ = ( _estimate_gaussian_parameters ( X , np . exp ( log_resp ) , self . reg_covar , self . covariance_type ) ) self . weights_ /= n_samples self . precisions_cholesky_ = _compute_precision_cholesky ( self . covariances_ , self . covariance_type )