parameters: (self, X)
return_statement: return mean_proba
docstring: Posterior probabilities of classification
  
          This function returns posterior probabilities of classification
          according to each class on an array of test vectors X.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The samples.
  
          Returns
          -------
          C : array, shape (n_samples, n_classes)
              The predicted probas.
docstring_summary: Posterior probabilities of classification
docstring_tokens: Posterior probabilities of classification
function: def predict_proba(self, X):
          """Posterior probabilities of classification
  
          This function returns posterior probabilities of classification
          according to each class on an array of test vectors X.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              The samples.
  
          Returns
          -------
          C : array, shape (n_samples, n_classes)
              The predicted probas.
          """
          check_is_fitted(self, ["classes_", "calibrated_classifiers_"])
          X = check_array(X, accept_sparse=['csc', 'csr', 'coo'],
                          force_all_finite=False)
          # Compute the arithmetic mean of the predictions of the calibrated
          # classifiers
          mean_proba = np.zeros((X.shape[0], len(self.classes_)))
          for calibrated_classifier in self.calibrated_classifiers_:
              proba = calibrated_classifier.predict_proba(X)
              mean_proba += proba
  
          mean_proba /= len(self.calibrated_classifiers_)
  
          return mean_proba
function_tokens: def predict_proba ( self , X ) : check_is_fitted ( self , [ "classes_" , "calibrated_classifiers_" ] ) X = check_array ( X , accept_sparse = [ 'csc' , 'csr' , 'coo' ] , force_all_finite = False ) # Compute the arithmetic mean of the predictions of the calibrated # classifiers mean_proba = np . zeros ( ( X . shape [ 0 ] , len ( self . classes_ ) ) ) for calibrated_classifier in self . calibrated_classifiers_ : proba = calibrated_classifier . predict_proba ( X ) mean_proba += proba mean_proba /= len ( self . calibrated_classifiers_ ) return mean_proba