parameters: (self, X)
return_statement: return np.mean(log_prob_norm), log_resp
docstring: E step.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          Returns
          -------
          log_prob_norm : float
              Mean of the logarithms of the probabilities of each sample in X
  
          log_responsibility : array, shape (n_samples, n_components)
              Logarithm of the posterior probabilities (or responsibilities) of
              the point of each sample in X.
docstring_summary: E step.
docstring_tokens: E step .
function: def _e_step(self, X):
          """E step.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          Returns
          -------
          log_prob_norm : float
              Mean of the logarithms of the probabilities of each sample in X
  
          log_responsibility : array, shape (n_samples, n_components)
              Logarithm of the posterior probabilities (or responsibilities) of
              the point of each sample in X.
          """
          log_prob_norm, log_resp = self._estimate_log_prob_resp(X)
          return np.mean(log_prob_norm), log_resp
function_tokens: def _e_step ( self , X ) : log_prob_norm , log_resp = self . _estimate_log_prob_resp ( X ) return np . mean ( log_prob_norm ) , log_resp