#include "../src/NeuronNetwork/FeedForwardQuick"
#include "../src/NeuronNetwork/Learning/Reinforcement"
#include "../src/NeuronNetwork/Learning/OpticalBackPropagation"

#include <iostream>
#include <vector>

class X: public Shin::NeuronNetwork::Problem
{
	public:
		X(const X& a) :q(a.q) {}
		X(const std::vector<float> &a):q(a) {}
		std::vector<float> representation() const
		{
			return q;
		}
	protected:
		std::vector<float> q;
};

int main()
{
	srand(time(NULL));
	for (int test=0;test<3;test++)
	{
		Shin::NeuronNetwork::FeedForwardNetworkQuick q({2,6,1});
		Shin::NeuronNetwork::Learning::Reinforcement b(q);
		b.setPropagator(new Shin::NeuronNetwork::Learning::OpticalBackPropagation(q));
		b.getPropagator().setLearningCoeficient(0.9);
		b.getPropagator().allowEntropy();
		double targetQuality =1.7;
		if(test==2)
		{
			targetQuality =1.62;
			std::cerr << "Testing with OBP ...\n";

			b.setPropagator(new Shin::NeuronNetwork::Learning::OpticalBackPropagation(q));
			b.getPropagator().setLearningCoeficient(3);
		}
		b.setQualityFunction(
			[](const Shin::NeuronNetwork::Problem &pr,const Shin::NeuronNetwork::Solution &s)->float
			{
				std::vector <float> p=pr;
				float expect=0.0;
				if(p[0] && p[1])
					expect=0;
				else if(p[0] && !p[1])
					expect=1;
				else if(!p[0] && !p[1])
					expect=0;
				else if(!p[0] && p[1])
					expect=1;

//				std::cerr << "expected: " << expect << " got " << s[0];

				if(expect==0)
				{
					expect=0.33-s[0];
				}else
				{
					expect=s[0]-0.67;
				}

//				std::cerr << " returnning " << expect*5.0 << "\n";

				return expect*9.0;
			});

		std::vector<Shin::NeuronNetwork::Problem*> p;

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

		if(test==1)
		{
			std::cerr << "Testing with entropy ...\n";
			b.getPropagator().allowEntropy();
		}else
		{
			std::cerr << "Testing without entropy ...\n";
		}

		for(int i=0;i < 500000000;i++)
//		for(int i=0;i < 5;i++)
		{
			double err=b.learnSet(p);
		
			if(i%100000==0)
				srand(time(NULL));
			if(i%200000==0 || err > targetQuality)
			{
				std::cerr << i << " ("<< err <<").\n";
				for(int j=0;j<4;j++)
				{
					std::cerr << "\t" << j%4 << ". FOR: [" << p[j%4]->representation()[0] << "," <<p[j%4]->representation()[1] << "] res: " <<
							q.solve(*p[j%4])[0] << "\n";
				}
			}
			if(err >targetQuality)
				break;
		}
	}
}