#pragma once

#include <vector>
#include <random>

namespace NeuralNetwork {
	namespace ProblemSets {
		typedef std::pair<std::vector<float>, std::vector<float>> TrainingPattern;

		std::vector<TrainingPattern> Chess3X3(float min, std::size_t patterns) {
			std::vector<TrainingPattern> ret;
			std::mt19937 _generator(rand());
			std::uniform_real_distribution<> _distribution(min,1);
			float step = (1.0-min)/3.0;
			for(std::size_t i=0;i<patterns;i++) {
				float x=_distribution(_generator);
				float y=_distribution(_generator);
				int classX= (static_cast<int>((x-min)/step) + static_cast<int>((y-min)/step)) % 2;
				if(classX == 0) {
					ret.push_back({{x,y},{min}});
				} else {
					ret.push_back({{x,y},{1.0}});
				}
			}
			return ret;
		}

	}
}