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);
}
}
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);
}
}
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);
}
}