def task_4_results(): kf = KFold(n_folds=k) num_manual_features = len(X_manual[0]) accuracies = [] X_manual_np = np.asarray(X_manual) for train_index, test_index in kf.split(X_content): X_content_train = X_content[train_index] X_manual_test = X_manual[test_index] # Just for structure, populated below X_content_test = X_content[test_index] num_test = len(test_index) for i in range(num_manual_features): X_np_train = X_manual_np[train_index, i] SVM.learn_svm(X_content_train, list(X_np_train), prefix+"task_4_model") for j in range(num_test): X_manual_test[j][i] = SVM.load_svm(prefix+"task_4_model").predict([X_content_test[j]])[0] X_manual_train = X_manual[train_index] y_train = y[train_index] y_test = y[test_index] SVM.learn_svm(X_manual_train, y_train, prefix+"task_4_model_1") accuracies.append(SVM.test_svm_accuracy(X_manual_test, y_test, prefix+"task_4_model_1")) print "Accuracy for task_4:", np.mean(accuracies), "+-", np.std(accuracies) return accuracies