#include <iostream>
#include <cmath>
#include <ctime>
#include <cstdlib>

using namespace std;

double w0, w1, w2, w3, w4, w5; // wagi
double b1, b2, b3; // biasy

double random() // losuje 0.01 - 1.00
{
  return ((rand()%100)+1)/100.0;
}

double sigmoid(double x) // funkcja aktywuj¹ca
{
    return 1/(1+exp(-x));
}

double derivsigmoid(double x)
{
    return x*(1-x);
}

double mse_loss(double* y, double* ypred, int n) // n - liczba probek, ypred - wartosci obliczone, y - wartosci docelowe
{
    double result = 0;
    for(int i=0; i<n; i++)
        result+=pow((y[i]-ypred[i]), 2);
    return result/(double)n;
}

double feedforward(double* x) // wynik neuronu
{
    double h0 = sigmoid(w0*x[0] + w1*x[1] + b1);
    double h1 = sigmoid(w2*x[0] + w3*x[1] + b2);
    return sigmoid(w4*h0 + w5*h1 + b3);
}

void train(double* x, double* y, double learn_rate, int epochs, int n, int m) // m - size, n - co ile probek
{
    for(int i=0; i<epochs; i++)
    {

        for(int j=0; j<m; j++)
        {
            double h0 = sigmoid(w0*x[0] + w1*x[1] + b1);
            double h1 = sigmoid(w2*x[0] + w3*x[1] + b2);
            double o0 = sigmoid(w4*h0 + w5*h1 + b3);

            double deriv_mse = -2 * (y[j] - o0);

            double dW5 = h1 * derivsigmoid(o0);
            double dW4 = h0 * derivsigmoid(o0);
            double dB3 = derivsigmoid(o0);
            double dH0 = w4 * derivsigmoid(o0);
            double dH1 = w5 * derivsigmoid(o0);

            double dW0 = x[0] * derivsigmoid(h0);
            double dW1 = x[1] * derivsigmoid(h0);
            double dB1 = derivsigmoid(h0);

            double dW2 = x[0] = derivsigmoid(h1);
            double dW3 = x[1] = derivsigmoid(h1);
            double dB2 = derivsigmoid(h1);

        // aktualizacja wag
            w0 -= (deriv_mse*learn_rate*dH0*dW1);
            w1 -= (deriv_mse*learn_rate*dH0*dW2);
            b1 -= (deriv_mse*learn_rate*dH0*dB1);

            w2 -= (deriv_mse*learn_rate*dH1*dW3);
            w3 -= (deriv_mse*learn_rate*dH1*dW4);
            b2 -= (deriv_mse*learn_rate*dH1*dB2);

            w4 -= (deriv_mse*learn_rate*dW4);
            w5 -= (deriv_mse*learn_rate*dW5);
            b3 -= (deriv_mse*learn_rate*dB3);

        }
        if(i%n==0) // co ile n wypisac dane
        {
            double ypred[4];
            for(int j=0; j<4; j++)
            {
                ypred[j] = feedforward(x[j]);
            }
            double loss = mse_loss(y, ypred, 4);


            cout << "epochs: " << i << endl;
            for(int j=0; j<4; j++)
                cout << ypred[j] << endl;
            cout << loss << endl;

            //print mse, ypred
        }
    }

}

int main()
{
    srand(time(NULL));
    w0 = random();
    w1 = random();
    w2 = random();
    w3 = random();
    w4 = random();
    w5 = random();
    b1 = random();
    b2 = random();
    b3 = random();

    cout << w1 << endl;

    return 0;

}