'__main__':
    warnings.filterwarnings('ignore', category=ConvergenceWarning)

    learning_rate = float(input())
    epochs = int(input())
    train_x, train_y, test_x, test_y = divide_set(data)

    # probaj go ova so epochs namesto max_iter
    klasifikator = MLPClassifier(6, activation="tanh", max_iter=epochs, learning_rate_init=learning_rate,
                                 random_state=0)
    klasifikator.fit(train_x, train_y)

    train_acc = 0
    prediction = klasifikator.predict(train_x)
    for pred, ele in zip(prediction, train_y):
        if pred == ele:
            train_acc += 1

    test_acc = 0
    prediction = klasifikator.predict(test_x)
    for pred, ele in zip(prediction, test_y):
        if pred == ele:
            test_acc += 1

    train_acc = train_acc / len(train_x)
    test_acc = test_acc / len(test_x)

    if (int((train_acc - test_acc) * 100 > 15)):
        print("Se sluchuva overfitting")
    else:
        print("Ne se sluchuva overfitting")

    print("Tochnost so trenirachko mnozhestvo:", train_acc)
    print("Tochnost so validacisko mnozhestvo:", test_acc)