private static NeuroEvolveBot getBotGen(int genNumber, IRandomNumberGenerator <double> rand, NetworkType networkType)
{
var chromosome = (DoubleArrayChromosome)GetChromosomeGen(genNumber, rand, networkType);
var net = GetNetHelper.GetNet(chromosome, new int[] { 5, 4, 3 });
return(new NeuroEvolveBot(net, genNumber, genNumber.ToString()));
}
public static List <NeuroEvolveBot> GetBots(NetworkType networkType, IRandomNumberGenerator <double> rand)
{
var values = FileHandler.GetGenerations(networkType);
List <NeuroEvolveBot> bots = new List <NeuroEvolveBot>();
for (int i = 0; i < values.Count; i++)
{
var chrome = new DoubleArrayChromosome(rand, rand, rand, values[i]);
var net = GetNetHelper.GetNet(chrome, new int[] { 5, 4, 3 });
var bot = new NeuroEvolveBot(net, i, "NeuroEvolve" + " " + i);
bots.Add(bot);
}
return(bots);
}
public double Evaluate(IChromosome chromosone)
{
double score = 0;
var net = GetNetHelper.GetNet(chromosone, new int[] { 5, 4, 3 });
var GameRunner = new GameRunnerWithData();
foreach (var bot in bots)
{
var data = GameRunner.RunGame(bot, new NeuroEvolveBot(net, rand.Next(1, 256)));
score += GeneralScore(data.P2WinCount, data.P1WinCount, data);
}
double scaling = (double)bots.Count();
score = Math.Max(score + 1, 0.01) / scaling;
return(Math.Exp(score * 3));
}
static void Main(string[] args)
{
NetworkType chromType = GenTypes.ReLU;
var prevNeuralBots = new List <NeuroEvolveBot>();
var genValues = FileHandler.GetGenerations(GenTypes.ReLU);
var rand = new ZigguratExponentialGenerator();
var Bots = GetBots(chromType, rand);
prevNeuralBots.AddRange(Bots);
/*
* ;
*
* for (int i = 0; i < largestGen + 1; i++)
* {
* prevNeuralBots.Add(getBotGen(i, rand, chromType));
* }*/
var bots = new List <IBot> {
new DrawBot()
};
bots.AddRange(prevNeuralBots);
var bestChromeValues = FileHandler.GetGenerations(chromType);
DoubleArrayChromosome doubleChromosome = new DoubleArrayChromosome(rand, rand, rand, bestChromeValues[bestChromeValues.Count - 1]);
//DoubleArrayChromosome doubleChromosome = (DoubleArrayChromosome)GetChromosomeGen(largestGen, rand, chromType);
for (int i = 0; i < 10; i++)
{
var pop = new Population(200, doubleChromosome, new fitFuncForAdaptable(bots), new RouletteWheelSelection());
Console.WriteLine(pop.FitnessMax);
for (int j = 0; j < 10; j++)
{
pop.RunEpoch();
Console.WriteLine(pop.FitnessMax);
Console.WriteLine(pop.FitnessAvg);
}
var x = new fitFuncForAdaptable(bots).Evaluate(pop.BestChromosome);
doubleChromosome = (DoubleArrayChromosome)pop.BestChromosome;
var newNet = GetNetHelper.GetNet(doubleChromosome, new[] { 5, 4, 3 });
var newBot = new NeuroEvolveBot(newNet, 934 + i, "newBot" + i.ToString());
var gameRunner = new GameRunnerWithData();
GameData data;
foreach (var bot in bots)
{
data = gameRunner.RunGame(newBot, bot);
Console.WriteLine(data);
}
data = gameRunner.RunGame(newBot, new WaveBot());
Console.WriteLine(data);
bots.Add(newBot);
}
double[] values = doubleChromosome.Value;
FileHandler.WriteToLargestGen(chromType, values);
RunWaveTest();
}