clf_model = SVC(C=1) elif args.clf_model == 'ELMK': clf_model = elm.ELMKernel() elif args.clf_model == 'ELMR': clf_model = elm.ELMRandom() if args.clf_model in ['ELMK', 'ELMR']: train_elm_data = np.concatenate( (train_label[:, np.newaxis], train_data), axis=1) test_elm_data = np.concatenate( (test_label[:, np.newaxis], test_data), axis=1) clf_model.search_param(train_elm_data, cv="kfold", of="accuracy", eval=10) train_acc = clf_model.train(train_elm_data).get_accuracy() test_acc = clf_model.test(test_elm_data).get_accuracy() elif args.clf_model in ['pcafc', 'pcafc_sd']: train_acc, test_acc = deep_classify(train_data, test_data, train_label, test_label, num_signal_features, i_exp) else: train_pred, test_pred = classify(clf_model, train_data, train_label, test_data) # Calculate error train_acc = np.sum(train_pred == train_label) / len(train_pred) test_acc = np.sum(test_pred == test_label) / len(test_pred) dict_error['train_acc'].update(train_acc) dict_error['test_acc'].update(test_acc)