code: def score(self, features, classes, scoring_function=accuracy_score, **scoring_function_kwargs):
          
          if not self.mu:
              raise ValueError('The DistanceClassifier model must be fit before score() can be called')
  
          return scoring_function(classes, self.predict(features), **scoring_function_kwargs)
docs: Estimates the accuracy of the predictions from the constructed feature
  
          Parameters
          ----------
          features: array-like {n_samples, n_features}
              Feature matrix to predict from
          classes: array-like {n_samples}
              List of true class labels
  
          Returns
          -------
          accuracy_score: float
              The estimated accuracy based on the constructed feature