public XorEvolver(IFitnessTester fitnessTester)
{
this.fitnessTester = fitnessTester;
IEnumerable <NeuralNetwork> networks = Helpers.Repeat(
() => new NeuralNetwork(NeuralDna.Random(9), 2, 2, 1),
PopulationSize);
this.population = networks.ToDictionary(nn => nn, nn => this.fitnessTester.Fitness(nn));
}
public void ProcessGeneration()
{
List <NeuralNetwork> newNetworks = new List <NeuralNetwork>();
for (int i = 0; i < this.population.Count(); i++)
{
NeuralNetwork father = this.population.Keys.Random(nn => this.population[nn]);
NeuralNetwork mother = this.population.Keys.Except(new[] { father }).Random(nn => this.population[nn]);
NeuralDna childDna = father.Dna.Crossover(mother.Dna);
childDna.Mutate(MutationRate);
NeuralNetwork child = new NeuralNetwork(childDna, father.NumInputs, father.LayerNums);
newNetworks.Add(child);
}
this.population = newNetworks.ToDictionary(nn => nn, nn => this.fitnessTester.Fitness(nn));
}
public NeuralNetwork(NeuralDna dna, int numInputs, params int[] layerNums)
{
this.dna = dna;
this.numInputs = numInputs;
this.layerNums = layerNums;
Queue <double> genes = new Queue <double>(dna.Genes);
List <NeuronLayer> layers = new List <NeuronLayer>();
int previousLayer = numInputs;
foreach (int layerNum in layerNums)
{
layers.Add(new NeuronLayer(genes, layerNum, previousLayer));
previousLayer = layerNum;
}
if (genes.Any())
{
throw new ArgumentException();
}
this.layers = layers;
}