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

#include <iostream>

class X: public Shin::Problem
{
	protected:
		std::vector<float> representation() const
		{
			return std::vector<float>({1,1});
		}
};

class X1: public Shin::Problem
{
	protected:
		std::vector<float> representation() const
		{
			return std::vector<float>({1});
		}
};

int main()
{
	NeuralNetwork::FeedForward n({2,4,2});
	NeuralNetwork::FeedForward nq({2,4,2});
	if(n[2].size() != 4)
	{
		std::cout << "1) Actual size:" << n[1].size();
		return 1;
	}

	if(nq[1].size() != 4)
	{
		std::cout << "QUICK Actual size:" << nq[1].size();
		return 1;
	}

	n[2][0].setPotential(25);
	nq[2][0].setPotential(25);

	std::cout << "Potential: " << n[2][0].getPotential() << "\n";
	std::cout << "Potential: " << nq[2][0].getPotential() << "\n";

	Shin::Solution s =n.solve(X());
	Shin::Solution sq =nq.solve(X());

	if(s.size()!=2)
	{
		std::cout << "1";
		return 1;
	}

	for(int i=0;i<2;i++)
	{
		if(s[i]!=sq[i])
		{
			 std::cout << " 4 - " << i << " expected "<<s[i] << " was " <<sq[i];
			 return 1;
		}
	}
	n[2][0].setWeight(0,26.0);
	nq[2][0].setWeight(0,26.0);

	s =n.solve(X());
	sq =n.solve(X());

	if(s.size()!=2)
	{
		std::cout << "a1";
		return 1;
	}
	if(s[0]!=1)
	{
		std::cout << "a2";
		return 1;
	}
	if(s[1]!=1)
	{
		std::cout << "a3";
		return 1;
	}

	for(int i=0;i<2;i++)
	{
		if(s[i]!=sq[i])
		{
			 std::cout << " a4 - " << i << " expected "<<s[i] << " was " <<sq[i];
			 return 1;
		}
	}

	return 0;
}