private Dna(int[] outputsPerLayer, List <double> weightsAndBiases, List <int> activationIndexes, DnaHeritage origin = DnaHeritage.New)
{
Id = idCounter++;
OutputsPerLayer = new List <int>(outputsPerLayer);
WeightsAndBiases = new List <double>(weightsAndBiases);
ActivationIndexes = new List <int>(activationIndexes);
Heritage = origin;
}
public static Dna CloneAndMutate(Dna dna, DnaHeritage origin, float weightMutationPrevalence, float activationMutationPrevalence = 0)
{
if (!(weightMutationPrevalence > 0))
{
Debug.LogWarning("Attempted to mutate with a factor of zero");
return(Dna.Clone(dna));
}
List <double> mutatedWeightGene = dna.WeightsAndBiases.GuaranteedApplyToPercentage(weightMutationPrevalence, (weight) =>
{
double random01 = Random.Range(0f, 1f);
bool shouldScramble = random01 < 0.25f;
return(shouldScramble ? (random01 * 2) - 1 : weight *(0.5 + random01)); // scale by +/-50% OR random new value between -1 and 1
});
if (mutatedWeightGene.SequenceEqual(dna.WeightsAndBiases))
{
Debug.LogError("Failed to mutate weights???");
}
List <int> activationGene;
if (!(activationMutationPrevalence > 0))
{
activationGene = dna.ActivationIndexes;
}
else
{
int startIndexOfOutputLayerActivation = dna.ActivationIndexes.Count - dna.OutputsPerLayer.Last();
activationGene = dna.ActivationIndexes
.Take(startIndexOfOutputLayerActivation) // preserve the output layer activations
.ToList()
.GuaranteedApplyToPercentage(activationMutationPrevalence, (_) => Random.Range(0, Activation.FunctionsCount))
.Concat(dna.ActivationIndexes.Skip(startIndexOfOutputLayerActivation))
.ToList();
}
return(new Dna(dna.OutputsPerLayer.ToArray(), mutatedWeightGene, activationGene, origin));
}
