#include "../src/NeuralNetwork/FeedForward"

#include <iostream>
class X: public Shin::Problem
{
	public: X(bool x,bool y):Problem() {data.push_back(x);data.push_back(y);}
};

int main()
{
	srand(time(NULL));
	int lm=5;
	Shin::NeuralNetwork::FeedForward net({2,lm,1});
	bool x=1;
	int prev_err=0;
	int err=0;
	int l;
	int n;
	int w;
	int pot;
	int wei;
	int c=0;
	std::cout << "\ntest 1 & 1 -" << net.solve(X(1,1))[0];
	std::cout << "\ntest 1 & 0 -" << net.solve(X(1,0))[0];
	std::cout << "\ntest 0 & 1 - " << net.solve(X(0,1))[0];
	std::cout << "\ntest 0 & 0- " << net.solve(X(0,0))[0];
	std::cout << "\n---------------------------------------";
	do{
		if(c%10000 ==1)
		{
			std::cout << "\nmixed";
			srand(time(NULL));
		}
		err=0;
		c++;
		l=rand()%2+1;
		n=rand()%lm;
		w=rand()%2;
		if(l==2)
			n=0; 
		pot=net[l][n].getPotential();
		net[l][n].setPotential(pot*(rand()%21+90)/100);
		wei=net[l][n].getWeight(w);
		net[l][n].setWeight(w,wei*(rand()%21+90)/100);

		for(int i=0;i<100;i++)
		{
			bool x= rand()%2;
			bool y=rand()%2;
			Shin::Solution s =net.solve(X(x,y));
			if(s[0]!= (x xor y))
				err++;
		}

		if(err > prev_err)
		{
			net[l][n].setPotential(pot);
			net[l][n].setWeight(w,wei);
		};
		prev_err=err;
		if(err <1)
			x=0;
	}while(x);
	std::cout << "\ntest 1 & 1 -" << net.solve(X(1,1))[0];
	std::cout << "\ntest 1 & 0 -" << net.solve(X(1,0))[0];
	std::cout << "\ntest 0 & 1 - " << net.solve(X(0,1))[0];
	std::cout << "\ntest 0 & 0- " << net.solve(X(0,0))[0];
	std::cout << "\nTotaly: " << c << "\n";
}