private static float[][] SampleBestFitter(ParetoFrontPoint best, int sampleCount)
{
var sensorCount = best.Genome.Network.Sensors.Count;
var effectorCount = best.Genome.Network.Effectors.Count;
var sampleLen = sensorCount + effectorCount;
var samples = new float[sampleCount][];
for (var i = 0; i < sampleCount; i++)
{
samples[i] = new float[sampleLen];
var network = best.Genome.Network;
for (var s = 0; s < sensorCount; ++s)
{
network.Sensors[s] = RandomSource.Range(-1, 1);
samples[i][s] = network.Sensors[s];
}
network.Activate();
for (var e = 0; e < effectorCount; ++e)
{
samples[i][sensorCount + e] = network.Effectors[e];
}
}
return(samples);
}
public SplitConnectionWeightsSearch(
PopulationParameters populationParameters,
NetworkParameters networkParameters,
ReproductionParameters reproductionParameters)
{
_populationParameters = populationParameters;
_neatPopulation = new Population(networkParameters, reproductionParameters, false);
_genomes = Range(0, _populationParameters.PopulationSize)
.Select(_ => _neatPopulation.CreateInitialGenome());
_archiveSize = _populationParameters.PopulationSize / 2;
_archive = new List <ParetoFrontPoint>(_archiveSize);
WeightSamples = Range(0, 300)
.Select(_ => RandomSource.Range(0, MaxAbsWeightValue))
.ToArray();
InputSamples = Range(0, 60)
.Select(_ => RandomSource.Range(0, 1))
.ToArray();
}
public static float GetRandomUniform() => RandomSource.Range(-ConnectionGene.MaxAbsWeightValue, ConnectionGene.MaxAbsWeightValue);
