VI Treta Labaratoriska - 2

'__main__':
    x_1 = int(input())
    x_2 = int(input())

    # 1
    test_set_1 = dataset[:x_1]
    test_x_1 = [x[:-1] for x in test_set_1]
    test_y_1 = [x[-1] for x in test_set_1]

    train_set_1 = dataset[x_1:]
    train_x_1 = [x[:-1] for x in train_set_1]
    train_y_1 = [x[-1] for x in train_set_1]

    # 2
    test_set_2 = dataset[:x_2]
    test_x_2 = [x[:-1] for x in test_set_2]
    test_y_2 = [x[-1] for x in test_set_2]

    train_set_2 = dataset[x_2:]
    train_x_2 = [x[:-1] for x in train_set_2]
    train_y_2 = [x[-1] for x in train_set_2]

    classifier_1 = MLPClassifier(3, activation='relu', learning_rate_init=0.003, max_iter=200, random_state=0)
    classifier_2 = MLPClassifier(3, activation='relu', learning_rate_init=0.003, max_iter=200, random_state=0)

    classifier_1.fit(train_x_1, train_y_1)
    classifier_2.fit(train_x_2, train_y_2)

    accuracy_1 = 0
    for i in range(0, len(test_set_1)):
        predict = classifier_1.predict([test_x_1[i]])
        if predict[0] == test_set_1[i][-1]:
            accuracy_1 += 1
    final_acc_1 = accuracy_1 / len(test_set_1)
    print(f"Tochnost model1: {final_acc_1}")

    accuracy_2 = 0
    for i in range(0, len(test_set_2)):
        predict = classifier_2.predict([test_x_2[i]])
        if predict[0] == test_set_2[i][-1]:
            accuracy_2 += 1

    final_acc_2 = accuracy_2 / len(test_set_2)
    print(f"Tochnost model2: {final_acc_2}")

    if final_acc_1 > final_acc_2:
        print("Prviot model ima pogolema tochnost")
    elif final_acc_2 > final_acc_1:
        print("Vtoriot model ima pogolema tochnost")
    else:
        print("Dvata modeli imaat ednakva tochnost")