#include "../src/NeuronNetwork/Learning/QLearning.h"

#include <iostream>
#include <vector>


class X: public Shin::NeuronNetwork::Problem
{
	public:
		X(const X& a) :Problem(a) {}
		X(const std::vector<float> &a):Problem() {data=a;}
};

float atof(char *s)
{
    int f, m, sign, d=1;
    f = m = 0;

    sign = (s[0] == '-') ? -1 : 1;
    if (s[0] == '-' || s[0] == '+') s++;

    for (; *s != '.' && *s; s++) {
            f = (*s-'0') + f*10;
    }
    if (*s == '.')
            for (++s; *s; s++) {
                    m = (*s-'0') + m*10;
                    d *= 10;
            }
    return sign*(f + (float)m/d);
}

float AA=10;
float getQuality(X& p, int action)
{
	if((p[0]==0&& p[1]==0) ||(p[0]==1&& p[1]==1)) //should be 0
	{
 		return action==1?-AA:AA;
	}else // should be 1
	{
		return action==0?-AA:AA;
	}
}

int main(int argc, char **argv)
{
	srand(time(NULL));

	Shin::NeuronNetwork::Learning::QLearning l(2,45,2);
	if(argc==4 && argv[3][0]=='o')
	{
		std::cerr << "USING Optical Backpropagation\n";
		l.opticalBackPropagation();
	}
	if(argc>=3)
	{
		std::cerr << "Setting learning coefficients to:" << atof(argv[1]) << "," << atof(argv[2]) << "\n";
		l.setLearningCoeficient(atof(argv[1]),atof(argv[2]));
	}
	std::vector <std::pair<Shin::NeuronNetwork::Solution,Shin::NeuronNetwork::Problem>> p1x;

	std::vector <X> states;
	states.push_back(X(std::vector<float>({1,0})));
	states.push_back(X(std::vector<float>({0,0})));
	states.push_back(X(std::vector<float>({1,1})));
	states.push_back(X(std::vector<float>({0,1})));

	unsigned long step=0;
	double quality=0;
	while(step< 600000 && quality < (3.9*AA))
	{
		quality=0;
		if(step%10000==0)
			std::cerr << "STEP " << step << "\n";
		for(unsigned i=0;i<states.size();i++)
		{
			int choice=l.getChoice(states[i]);
			l.learn(states[i],choice,quality);
		}
		for(unsigned i=0;i<states.size();i++)
		{
			int choice=l.getChoice(states[i]);
			quality+=getQuality(states[i],choice);
			if(step%10000==0)
			{
				Shin::NeuronNetwork::Solution sol=l.getSolution(states[i]);
				std::cerr << "\tState: [" << states[i][0] << "," << states[i][1] << "] Q-function:  [" << sol[0] << "," <<sol[1] << "] Action " << choice << "\n";
			}
		}
		step++;
	}
	std::cerr << step << "\n";
	for(unsigned i=0;i<states.size();i++)
	{
		Shin::NeuronNetwork::Solution sol=l.getSolution(states[i]);
		int choice=l.getChoice(states[i]);
		std::cerr << "State: [" << states[i][0] << "," << states[i][1] << "] Q-function:  [" << sol[0] << "," <<sol[1] << "] Action " << choice << "\n";
	}
}