nevronska2

x = int(0.8 * len(data))
    training_set = dataset[:x]
    validation_set = dataset[x:]

    n_inputs = 15
    n_outputs = 2
    hidden = 6

    network = initialize_network(n_inputs, hidden, n_outputs)

    train_network(network, training_set, epoch_num, learning_rate, n_outputs, False)


    correct_train = 0
    correct_validation = 0


    for row in training_set:
        if predict(network, row) == row[-1]:
                correct_train += 1

    accuracy_train = correct_train / len(training_set)

    for row in validation_set:
        if predict(network, row) == row[-1]:
                correct_validation += 1

    accuracy_validation = correct_validation / len(validation_set)

    difference = accuracy_train - accuracy_validation

    if(difference > 0.15):
        print("Overfitting")
    else:
        print("Ne se sluchuva overfitting")

    print("Tochnost so trenirachko mnozestvo:", accuracy_train)
    print("Tochnost so validacisko mnozestvo:", accuracy_validation)