#include <iostream>
#include <fstream>
#include "board2.h"
using namespace std;

class GA{
private:
	int population[POP_SIZE][NUM_FEATURES];
public:
	GA();
	int fitness(int &pop_id, int &gen);
	void create_child(int child[NUM_FEATURES], int parent1[NUM_FEATURES], int parent2[NUM_FEATURES]);
	void evolve();
};

//GA constructor
GA::GA(){
	for(int i=0; i<POP_SIZE; i++){
		for(int j=0; j<NUM_FEATURES; j++)
			population[i][j] = rand()%100;
	}
}

//Find the fitness of an individual
int GA::fitness(int &pop_id, int &gen){
	//Simulate a number of games
	int fit_stones = 0;
	for(int i=0; i<NUM_FITNESS_GAMES; i++){
		//Alternate which player goes first
		Player *p1;
		Player *p2;
		Player *testing_player;
		int lookahead = 4;
			
		if(i%2){
			//Player 1 with pop_id
			p1 = new Player(population[pop_id], 1);
			testing_player = p1;
			//Player 2 with random individual
			p2 = new Player(lookahead, 2);
		}
		else{
			//Player 1 with random individual
			p1 = new Player(lookahead, 1);
			//Player 2 with pop_id
			p2 = new Player(population[pop_id], 2);
			testing_player = p2;
		}
		//Simulate game
		Mancala board(p1,p2);
		board.run_game(true);
		fit_stones += board.num_stones(testing_player);
		delete p1;
		delete p2;
	}
	//Return the number of games won
	return fit_stones;
}

//Create a mutated version of parent
void GA::create_child(int child[NUM_FEATURES],int parent1[NUM_FEATURES], int parent2[NUM_FEATURES]){
	int split = rand()%NUM_FEATURES;
	if(split >= (NUM_FEATURES/2)){
		for(int i=0; i<=split; i++)
			child[i] = parent1[i];
		for(int i=split+1; i<NUM_FEATURES; i++)
			child[i] = parent2[i];
	}
	else{
		for(int i=0; i<split; i++)
			child[i] = parent2[i];
		for(int i=split; i<NUM_FEATURES; i++)
			child[i] = parent1[i];
	}
	for(int i=0; i<NUM_FEATURES; i++){
		bool mutate = false;
		if( ((float)rand()/RAND_MAX) < MU )
			mutate = true;
		if(mutate){
			if(rand()%2)
				child[i]++;
			else
				child[i]--;
		}
		
		if(child[i]>400)
			child[i] = 400;
		if(child[i]<1)
			child[i] = 1;
	}
}

//Run the evolutionary process
void GA::evolve(){
	string file = "outfile.txt";
	ofstream outfile (file.c_str());
	if(!outfile){
		cout << "\nThere was an error in creating an output file." << endl;
		exit(0);
	}
	int average[NUM_FEATURES] = {0};
	int best[NUM_FEATURES] = {0};
	//For a number of generations
	for(int gen=0; gen<NUM_GENS; gen++)
	{
		//Compute the fitness of the individuals
		int pop_fitness[POP_SIZE] = {0};
		int total_pop_fitness = 0;
		int best_gen_fitness = 0;
		for(int i=0; i<POP_SIZE; i++){
			pop_fitness[i] = fitness(i, gen);
			total_pop_fitness += pop_fitness[i];
			if(pop_fitness[i]>best_gen_fitness){
				best_gen_fitness = pop_fitness[i];
				for(int j=0; j<NUM_FEATURES; j++)
					best[j] = population[i][j];
			}
		}
		cout << "\n Generation " << gen << " best fitness: " << best_gen_fitness
			<< " avg: " << (float)total_pop_fitness/POP_SIZE;
		//Perform fitness proportional selection
		float spoke = ((float)rand()/RAND_MAX);
		float increment = ((float)1.0/POP_SIZE);
		while(spoke<1.0){
			spoke += increment;
		}
		spoke -= 1.0;
		int num_children[POP_SIZE] = {0};
		float current_sum = 0.0;
		int i = 0;
		int correctness_checker = 0;
		while(i < POP_SIZE){
			current_sum += (float)((float)pop_fitness[i]/(float)total_pop_fitness);
			while(current_sum >= spoke){
				num_children[i]++;
				correctness_checker++;
				spoke += increment;
			}
			i++;
		}
		if(correctness_checker!=POP_SIZE){
			if(correctness_checker>POP_SIZE){
				while(correctness_checker>POP_SIZE){
					int x = rand()%POP_SIZE;
					while(num_children[x]==0)
						x = rand()%POP_SIZE;
					num_children[x]--;
					correctness_checker--;
				}
			}
			else{
				cout << "\nThere was an error in the roulette wheel section! aborting" << endl;
				exit(0);
			}
		}
		//Create a child population
		int child_pop[POP_SIZE][NUM_FEATURES];
		for(int child_i=0; child_i<POP_SIZE; child_i++){
			int parent_i=0;
			while(num_children[parent_i]==0 && parent_i<POP_SIZE){
				parent_i++;
			}
			num_children[parent_i]--;
			create_child(child_pop[child_i],population[parent_i],population[rand()%POP_SIZE]);
		}
		//Replace parents with children
		for(int i=0; i<POP_SIZE; i++){
			for(int j=0; j<NUM_FEATURES; j++)
				population[i][j] = child_pop[i][j];
		}
		//Find the average of this population
		for(int i=0; i<NUM_FEATURES; i++)
            average[i] = 0;
		cout << "\nAVG:" << endl;
		for(int i=0; i<POP_SIZE; i++){
			for(int j=0; j<NUM_FEATURES; j++)
				average[j] += population[i][j];
		}		
		for(int i=0; i<NUM_FEATURES; i++)
			average[i] = (int)average[i]/POP_SIZE;
		for(int i=0; i<5; i++){
			for(int j=0; j<6; j++)
				cout << " " << average[(6*i)+j];
			cout << endl;
		}
	}//End of generation loop
	//Outfile
	for(int i=0; i<5; i++){
		for(int j=0; j<6; j++)
			outfile << " " << best[(6*i)+j];
		outfile << endl;
	}
}