public void TestZigguratExponentialGenerator_Unseeded_InitializesCorrectly()
{
var rng = new ZigguratExponentialGenerator();
double num = rng.Generate();
Assert.NotNull(rng);
}
public void TestZigguratExponentialGenerator_WithSeed_InitializesCorrectly()
{
var rng1 = new ZigguratExponentialGenerator(seed: 457860009);
var rng2 = new ZigguratExponentialGenerator(seed: 457860009);
double num1 = rng1.Generate();
double num2 = rng2.Generate();
Assert.AreEqual(num1, num2);
}
public void TestZigguratExponentialGenerator_Unseeded_NeverOverflows()
{
var rng = new ZigguratExponentialGenerator();
for (int i = 0; i < 100; i++)
{
double num = rng.Generate();
}
Assert.NotNull(rng);
}
private static void RunWaveTest()
{
var prevNeuralBots = new List <NeuroEvolveBot>();
var rand = new ZigguratExponentialGenerator();
var gens = GetBots(GenTypes.ReLU, rand);
var leagueRunner = new LeagueRunner();
foreach (var bot in gens)
{
var data = LeagueRunner.RunLeague(new WaveBot(), bot);
Console.WriteLine(data);
}
}
public static void RunGene()
{
var rand = new ZigguratExponentialGenerator();
var chrom = new DoubleArrayChromosome(rand, rand, rand, 9);
var pop = new Population(1000, chrom, new fitFunc(), new EliteSelection());
Console.WriteLine(pop.FitnessMax);
for (int i = 0; i < 199; i++)
{
pop.RunEpoch();
Console.WriteLine(pop.FitnessMax);
}
var x = new fitFunc().Evaluate(pop.BestChromosome);
Console.WriteLine(pop.FitnessMax);
}
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();
}
