private float fitness(CPPN cppn, int evaluationIterations)
{
Vector2 vel = new Vector2();
Vector2 prevVel = new Vector2();
Vector2 pos = new Vector2();
float traveledDistance = 0;
for (int i = 0; i < evaluationIterations; i++)
{
prevVel = new Vector2(vel.x, vel.y);
float[] input = { vel[0], vel[1], traveledDistance };
float[] result = cppn.setInputGetoutput(input);
Vector2 res = new Vector2(result[0], result[1]);
if (res.magnitude > maxSpeed)
{
res *= maxSpeed / res.magnitude;
}
vel[0] += Mathf.Clamp(res[0], -maxSpeed * Time.deltaTime, maxSpeed * Time.deltaTime);
vel[1] += Mathf.Clamp(res[1], -maxSpeed * Time.deltaTime, maxSpeed * Time.deltaTime);
traveledDistance += vel.magnitude;
pos += vel;
}
return(traveledDistance / (pos.magnitude * 0.5f));
}
private void Start()
{
init();
CPPN winner = createRandomAndGetWinner(100, 1000);
spawnWinner(winner);
StartCoroutine(autoSpawner(winner));
}
public static void RunEvolution2()
{
var bmp = new System.Drawing.Bitmap(FILE_IN);
ImageSys img = new ImageSys(bmp);
Texture txt = new Interpolent(img, Intpol.Nearest);
//var fitness = BuildFitness(txt);
Console.WriteLine("Collecting Sample Points...");
ControlPoints.BuildData();
Fitness <CPPN> fitness = x => ControlPoints.GradeImage(x);
EvolSpecies <CPPN> evolver = new EvolSpecies <CPPN>(500, 1.0, fitness); //100, 0.1
CPPN best = new CPPN();
CPPN proto = new CPPN();
Renderor ren = new Renderor();
ImageSys output = new ImageSys(256, 256);
//img.BMP.Save(FILE_OUT + "test.png");
Console.WriteLine("Aproximating Image: " + FILE_IN);
evolver.Initialise(best);
for (int i = 0; i < MAX_GEN; i++)
{
Console.WriteLine();
string index = i.ToString("0000");
Console.WriteLine("Rendering Best Fit...");
ren.Render(best, output);
output.BMP.Save(FILE_OUT + "A" + index + ".png");
Console.WriteLine("Rendering Random Species...");
ren.Render(proto, output);
output.BMP.Save(FILE_OUT + "B" + index + ".png");
int nodes = best.NumNurons;
int axons = best.NumAxons;
double fit = evolver.TopFitness;
Console.WriteLine();
Console.WriteLine("Generation " + i + ": " + nodes + " " + axons + " " + fit);
Console.WriteLine("Num Species: " + evolver.NumSpecies);
Console.WriteLine("Threshold: " + evolver.Threshold.ToString("0.00"));
////we cannot have more species than there are indvidual organisms
//if (evolver.NumSpecies > 100) throw new Exception();
evolver.Evolve();
evolver.GetTopSpec(best);
evolver.GetRandProto(proto);
}
}
IEnumerator autoSpawner(CPPN cppn)
{
CPPN winner = cppn;
while (true)
{
yield return(new WaitForSeconds(2));
winner = createFromWinnerAndGetWinner(winner, 100, 1000);
spawnWinner(winner);
}
}
public static void RandomMutaitons()
{
var bmp = new System.Drawing.Bitmap(FILE_IN);
ImageSys img = new ImageSys(bmp);
Texture txt = new Interpolent(img, Intpol.Nearest);
var fitness = BuildFitness(txt);
CPPN network = new CPPN();
VRandom rng = new RandMT();
network.Randomize(rng);
Renderor ren = new Renderor();
ImageSys output = new ImageSys(256, 256);
//img.BMP.Save(FILE_OUT + "test.png");
Console.WriteLine("Building Network...");
for (int i = 0; i < 200; i++)
{
network.Mutate(rng, 1.0);
}
Console.WriteLine("Aproximating Image: " + FILE_IN);
//NOTE: Make it so we don't accept mutations that
//result in NaN fitness!
for (int i = 0; i < MAX_GEN; i++)
{
//network.Mutate(rng, 1.0); //0.1
network.Randomize(rng);
ren.Render(network, output);
string file = i.ToString("0000") + ".png";
output.BMP.Save(FILE_OUT + file);
int nodes = network.NumNurons;
int axons = network.NumAxons;
double fit = fitness(network);
//Console.WriteLine();
Console.WriteLine("Generation " + i + ": " + nodes + " " + axons + " " + fit);
}
}
private CPPN createRandomAndGetWinner(int amount, int evaluationIterations)
{
CPPN winner = null;
float winnerScore = 0;
for (int i = 0; i < amount; i++)
{
CPPN cppn = new CPPN(3, 2, neuronIA, connectionIA);
float score = fitness(cppn, evaluationIterations);
if (score > winnerScore)
{
winner = cppn;
winnerScore = score;
}
}
Debug.Log("Won with " + winnerScore);
return(winner);
}
public float Fitness(CPPN cppn, int evaluationIterations)
{
//float output = 0;
Vector2 vel = new Vector2();
Vector2 pos = new Vector2();
float traveledDistance = 0;
for (int i = 0; i < evaluationIterations; i++)
{
float[] input = { vel[0], vel[1], traveledDistance };
float[] result = cppn.setInputGetoutput(input);
vel[0] = Mathf.Clamp(result[0], -maxSpeed * Time.deltaTime, maxSpeed * Time.deltaTime);
vel[1] = Mathf.Clamp(result[1], -maxSpeed * Time.deltaTime, maxSpeed * Time.deltaTime);
traveledDistance += vel.magnitude;
pos += vel;
}
return(pos.magnitude + traveledDistance);
}
private void Start()
{
int seed = Random.Range(0, 10000);
Random.InitState(seed);
Debug.Log("Seed: " + seed);
CPPN cppn1 = new CPPN(3, 2, neuronIA, connectionIA);
cppn1.printGenome();
CPPN cppn2 = new CPPN(3, 2, neuronIA, connectionIA);
cppn2.printGenome();
CPPN.Genome genome = CPPNLib.Crossover(cppn1.genome, cppn2.genome);
genome.mutate(neuronIA, connectionIA);
cppn = new CPPN(genome, neuronIA, connectionIA);
cppn.printGenome();
Debug.Log("fitness: " + Fitness(cppn, 100));
rb = GetComponent <Rigidbody>();
}
private CPPN createFromWinnerAndGetWinner(CPPN cppn, int amount, int evaluationIterations)
{
CPPN winner = cppn;
float winnerScore = fitness(cppn, evaluationIterations);
for (int i = 0; i < amount; i++)
{
CPPN.Genome genome = cppn.genome.copy();
genome.mutate(neuronIA, connectionIA);
CPPN newCppn = new CPPN(genome, neuronIA, connectionIA);
float score = fitness(newCppn, evaluationIterations);
if (score > winnerScore)
{
winner = newCppn;
winnerScore = score;
}
}
Debug.Log("Won with " + winnerScore);
return(winner);
}
public static void RunEvolution()
{
var bmp = new System.Drawing.Bitmap(FILE_IN);
ImageSys img = new ImageSys(bmp);
Texture txt = new Interpolent(img, Intpol.Nearest);
var fitness = BuildFitness(txt);
EvolMonogen <CPPN> evolver = new EvolMonogen <CPPN>(50, 0.1, fitness);
CPPN best = new CPPN();
evolver.Initialise(best);
Renderor ren = new Renderor();
ImageSys output = new ImageSys(256, 256);
//img.BMP.Save(FILE_OUT + "test.png");
Console.WriteLine("Aproximating Image: " + FILE_IN);
for (int i = 0; i < MAX_GEN; i++)
{
evolver.Evolve();
evolver.GetTopSpec(best);
ren.Render(best, output);
string index = i.ToString("0000");
output.BMP.Save(FILE_OUT + index + ".png");
int nodes = best.NumNurons;
int axons = best.NumAxons;
double fit = evolver.TopFitness;
Console.WriteLine();
Console.WriteLine("Generation " + i + ": " + nodes + " " + axons + " " + fit);
}
}
public void setup(CPPN icppn, float imaxSpeed)
{
rb = GetComponent <Rigidbody>();
cppn = icppn;
maxSpeed = imaxSpeed;
}
private void spawnWinner(CPPN cppn)
{
ParticleController pc = Instantiate(particlePrefab).GetComponent <ParticleController>();
pc.setup(cppn, maxSpeed);
}
