classifier = KNearestNeighbor()
for i, k in enumerate(k_choices):
    k_accuracy = np.zeros(num_folds)
    for j in range(num_folds):
        X_train =
        classifier.train(cross_X_train, cross_y_train)
        dists = classifier.compute_distances_no_loops(X_train_folds[j])
        cross_y_test_pred = classifier.predict_labels(dists, k)
        cross_num_correct = np.sum(cross_y_test_pred == y_train_folds[j])
        cross_accuracy = float(num_correct) / (X_train_folds[j].shape[0])
        k_accuracy[j] = cross_accuracy
    k_to_accuracies[k] = k_accuracy