code: def example_of_correlating_two_components(
      raw_data, raw_data2, labels, num_subjects, num_epochs_per_subj
  ):
      # aggregate the kernel matrix to save memory
      svm_clf = svm.SVC(
          kernel="precomputed", shrinking=False, C=1, gamma="auto"
      )
      clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
      num_training_samples = num_epochs_per_subj * (num_subjects - 1)
      clf.fit(
          list(
              zip(
                  raw_data[0:num_training_samples],
                  raw_data2[0:num_training_samples],
              )
          ),
          labels[0:num_training_samples],
      )
      X = list(
          zip(
              raw_data[num_training_samples:],
              raw_data2[num_training_samples:],
          )
      )
      predict = clf.predict(X)
      print(predict)
      print(clf.decision_function(X))
      test_labels = labels[num_training_samples:]
      incorrect_predict = (
          hamming(predict, np.asanyarray(test_labels))
          * num_epochs_per_subj
      )
      logger.info(
          "when aggregating the similarity matrix to save memory, "
          "the accuracy is %d / %d = %.2f"
          % (
              num_epochs_per_subj - incorrect_predict,
              num_epochs_per_subj,
              (num_epochs_per_subj - incorrect_predict)
              * 1.0
              / num_epochs_per_subj,
          )
      )
      # when the kernel matrix is computed in portion, the test data is already in
      print(clf.score(X, test_labels))
  
file_path: brainiak/examples/fcma/classification.py
imports: from brainiak.fcma.classifier import Classifier
  from brainiak.fcma.preprocessing import prepare_fcma_data
  from brainiak import io
  from sklearn import svm
  import sys
  import logging
  import numpy
  from scipy.spatial.distance import hamming
  from sklearn import model_selection
label_desc: Use image node_id 4 for calling a global function with example usage: example_of_correlating_two_components(raw_data, raw_data2, labels, num_subjects, num_epochs_per_subj) without return types