// This file is a part of Framsticks SDK.  http://www.framsticks.com/
// Copyright (C) 2019-2020  Maciej Komosinski and Szymon Ulatowski.
// See LICENSE.txt for details.


#include <vector>
#include "common/loggers/loggertostdout.h"
#include "frams/genetics/preconfigured.h"
#include "frams/genetics/genman.h"
#include "frams/model/model.h"


struct Individual
{
	Geno geno;
	double fitness;
};

double criterion(char symbol, double value)
{
	return isupper(symbol) ? value : -value;
}

void evaluate_fitness(Individual &ind, const char *fitness_def)
{
	SString genotype = ind.geno.getGenes();
	Model model = Model(ind.geno, Model::SHAPETYPE_UNKNOWN);
	double fitness = 0;
	const char *p = fitness_def;
	while (*p)
	{
		switch (*p)
		{
		case 'l':
		case 'L':
			fitness += criterion(*p, genotype.length());
			break;
		case 'p':
		case 'P':
			fitness += criterion(*p, model.getPartCount());
			break;
		case 'j':
		case 'J':
			fitness += criterion(*p, model.getJointCount());
			break;
		case 'n':
		case 'N':
			fitness += criterion(*p, model.getNeuroCount());
			break;
		case 'c':
		case 'C':
			fitness += criterion(*p, model.getConnectionCount());
			break;
			// TODO add more criteria as described in main() below
		default:
			printf("Unknown fitness criterion symbol: '%c'\n", *p);
			exit(3);
		}
		p++;
	}
	ind.fitness = fitness;
}

int tournament(const vector<Individual> &population, int tournament_size)
{
	int best = -1;
	for (int i = 0; i < tournament_size; i++)
	{
		int rnd = rndUint(population.size());
		if (best == -1) best = rnd;
		else if (population[rnd].fitness > population[best].fitness) //assume maximization
			best = rnd;
	}
	return best;
}


/** A minimalistic steady-state evolutionary algorithm. */
int main(int argc, char *argv[])
{
	PreconfiguredGenetics genetics;
	LoggerToStdout messages_to_stdout(LoggerBase::Enable);
	GenMan genman;

	bool deterministic;
	int pop_size, nr_evals;
	double prob_mut, prob_xover;
	const char* format;
	const char* fitness_def;

	if (argc < 8)
	{
		printf("Too few parameters!\n");
		printf("Command line: <deterministic?_0_or_1> <population_size> <nr_evaluations> <prob_mut> <prob_xover> <genetic_format> <fitness_definition>\n");
		printf("Example: 1 10 50 0.5 0.5 4 NC\n\n\n");
		printf("Fitness definition is a sequence of capital (+1 weight) and small (-1 weight) letters.\n");
		printf("Each letter corresponds to one fitness criterion, and they are all weighted and added together.\n");
		printf("  l or L - genotype length in characters.\n");
		printf("  p or P - the number of Parts.\n");
		printf("  j or J - the number of Joints.\n");
		printf("  n or N - the number of Neurons.\n");
		printf("  c or C - the number of neural Connections.\n");
		//TODO add b - bounding box volume (from Model), s - surface area (from geometry), v - volume (from geometry), h,w,d - three consecutive dimensions (from geometry)

		return 1;
	}

	deterministic = atoi(argv[1]) == 1;
	pop_size = atoi(argv[2]);
	nr_evals = atoi(argv[3]);
	prob_mut = atof(argv[4]);
	prob_xover = atof(argv[5]);
	format = argv[6];
	fitness_def = argv[7];

	if (!deterministic)
		rndGetInstance().randomize();

	vector<Individual> population(pop_size);
	for (Individual& ind : population)
	{
		ind.geno = genman.getSimplest(format);
		if (ind.geno.getGenes() == "")
		{
			printf("Could not get the simplest genotype for format '%s'\n", format);
			return 2;
		}
		evaluate_fitness(ind, fitness_def);
	}
	for (int i = 0; i < nr_evals; i++)
	{
		int selected_positive = tournament(population, max(2, int(sqrt(population.size()) / 2))); //moderate positive selection pressure
		int selected_negative = rndUint(population.size()); //random negative selection

		double rnd = rndDouble(prob_mut + prob_xover);
		if (rnd < prob_mut)
		{
			population[selected_negative].geno = genman.mutate(population[selected_positive].geno);
			//TODO handle failed mutation
			evaluate_fitness(population[selected_negative], fitness_def);
		}
		else
		{
			//TODO crossover
		}

		if (i % population.size() == 0 || i == nr_evals - 1)
			printf("Evaluation %d\t...\n", i); //TODO print min,avg,max fitness \t min,avg,max genotype length \t min,avg,max parts \t min,avg,max neurons
	}
	for (const Individual& ind : population)
	{
		printf("%.1f\t", ind.fitness);
		printf("%s\n", ind.geno.getGenes().c_str());
	}

	return 0;
}