/// <summary>
/// info to generate new random networks is used to generate new species
/// </summary>
public NetworkEvolution(List <EvolutionaryLine <Game> > species, int inputLenght, int outputLenght, int startingSpecieNetworkCount, TemporalNetwork.ActivationFunctions activation = TemporalNetwork.ActivationFunctions.Sine, int layersCount = 1, int minNeurons = 1, int maxNeurons = 6)
{
this.startingSpecieNetworksCount = startingSpecieNetworkCount;
this.species = species;
this.activation = activation;
this.inputLenght = inputLenght;
this.outputLenght = outputLenght;
startingLayersCount = layersCount;
this.minNeurons = minNeurons;
this.maxNeurons = maxNeurons;
}
public NetworkEvolution(Game startingState, int speciesCount, int initialSpecieNetworkCount, MutationInfo mutationInfo, TemporalNetwork.ActivationFunctions activation, int inputLenght, int outputLenght, int layersCount, int minNeurons, int maxNeurons)
{
startingSpecieNetworksCount = initialSpecieNetworkCount;
this.startingState = startingState;
this.activation = activation;
this.inputLenght = inputLenght;
this.outputLenght = outputLenght;
this.startingLayersCount = layersCount;
this.minNeurons = minNeurons;
this.maxNeurons = maxNeurons;
species = new List <EvolutionaryLine <Game> >();
for (int i = 0; i < speciesCount; i++)
{
species.Add(new EvolutionaryLine <Game>(startingState, startingSpecieNetworksCount, mutationInfo, activation, inputLenght, outputLenght, layersCount, minNeurons, maxNeurons));
}
}
public FittedNetwork(Game game, MutationInfo mutationInfo, TemporalNetwork.ActivationFunctions activationFunction, int inputLenght, int outputLenght, int layersCount, int minNeurons, int maxNeurons)
{
this.game = game;
network = new TemporalNetwork(activationFunction, inputLenght, outputLenght, layersCount, minNeurons, maxNeurons, mutationInfo);
fitnessScore = 0;
}
/// <summary>
/// Starts a new family tree with a random biased network
/// </summary>
public EvolutionaryLine(Game startingState, int startingNetworkCount, MutationInfo mutationInfo, TemporalNetwork.ActivationFunctions activationFunction, int inputLenght, int outputLenght, int layersCount, int minNeurons, int maxNeurons)
{
networks = new List <FittedNetwork <Game> >();
for (int i = 0; i < startingNetworkCount; i++)
{
networks.Add(new FittedNetwork <Game>(startingState, mutationInfo, activationFunction, inputLenght, outputLenght, layersCount, minNeurons, maxNeurons));
}
}
