# Your code goes here
# 5 for training 5 for testing
def face_prediction(samples):
    # split the data
    training_samples, testing_samples= partition_data(labels,num_per_class=5)

    # split testing and traing samples to get X and Y
    training_data_X, training_data_Y = split_left_right(data[training_samples])
    testing_data_X, testing_data_Y = split_left_right(data[testing_samples])

    # train the model
    w = l2_rls_train(training_data_X,training_data_Y,0)

    # make predictions
    predictions = l2_rls_predict(w,testing_data_X)

    # join the predicted faces together
    faces = join_left_right(testing_data_X, predictions)

    # join the test faces together for comparing
    real_faces = join_left_right(testing_data_X, testing_data_Y)

    print(np.size(predictions))
    print(np.abs(testing_data_Y- predictions))
    errors = np.sum(np.abs(testing_data_Y - predictions)) * 100 / (255 * np.size(predictions))
    print(errors)
    # show both sets of faces
    show_faces(faces[20:30, :], num_per_row=5)
    show_faces(real_faces[20:30, :], num_per_row=5)