code: def score(self, X, y, sample_weight=None):
          
          from sklearn.metrics import accuracy_score
          if isinstance(self.clf, sklearn.svm.SVC) \
                  and self.clf.kernel == 'precomputed' \
                  and self.training_data_ is None:
              result = accuracy_score(y, self.predict(),
                                      sample_weight=sample_weight)
          else:
              result = accuracy_score(y, self.predict(X),
                                      sample_weight=sample_weight)
          return result
docs: Returns the mean accuracy on the given test data and labels.
  
          NOTE: In the condition of sklearn.svm.SVC with precomputed kernel
          when the kernel matrix is computed portion by portion, the function
          will ignore the first input argument X.
  
          Parameters
          ----------
          X: list of tuple (data1, data2)
              data1 and data2 are numpy array in shape [num_TRs, num_voxels]
              to be computed for correlation.
              They are test samples.
              They contain the activity data filtered by ROIs
              and prepared for correlation computation.
              Within list, all data1s must have the same num_voxels value,
              all data2s must have the same num_voxels value.
              len(X) is the number of test samples.
  
          y: 1D numpy array
              labels, len(X) equals len(y), which is num_samples
          sample_weight: 1D array in shape [num_samples], optional
              Sample weights.
  
          Returns
          -------
          score : float
              Mean accuracy of self.predict(X) wrt. y.