/// <summary>
/// Create next generation.
/// </summary>
/// <returns>Next generation NeuralNetwork.</returns>
public NeuralNetwork NextGeneration()
{
NeuralNetwork rtnn = new NeuralNetwork(sourceNetwork);
//create new
if (first)
{
rtnn.CopyWeightsAndBiases(sourceNetwork);
first = false;
}
else
{
if (breeding && bestLoss <= maxBreedingLoss)
{
if (bestNetwork != null && secondBestNetwork != null)
{
rtnn.CopyWeightsAndBiases(bestNetwork);
rtnn.Breed(secondBestNetwork);
if (lossDelta > 0.0f)
{
rtnn.Mutate(lossDelta);
}
}
else
{
rtnn.RandomizeWeightsAndBiases();
}
}
else
{
rtnn.RandomizeWeightsAndBiases();
}
}
return(rtnn);
}
/// <summary>
/// Record NeuralNetworks loss and create next generation.
/// </summary>
/// <param name="nn"></param>
/// <param name="loss"></param>
/// <returns>Next generation NeuralNetwork.</returns>
public NeuralNetwork NextGeneration(NeuralNetwork nn, float loss)
{
NeuralNetwork rtnn = nn;
if (first)
{
rtnn.CopyWeightsAndBiases(sourceNetwork);
first = false;
}
else
{
if (loss <= 1.0f)
{
lock (breedLock)
{
//returned performance, record performance and mutate new network
lossDelta += mutationIncreaseRate;
if (lossDelta > maxMutationRate)
{
lossDelta = maxMutationRate;
}
if (loss < bestLoss)
{
//new best loss
lossDelta = minMutationRate;
if (breeding)
{
secondBestLoss = bestLoss;
secondBestNetwork = bestNetwork;
}
bestLoss = loss;
bestNetwork = nn;
rtnn = new NeuralNetwork(nn);
}
else if (breeding && loss < secondBestLoss)
{
secondBestLoss = loss;
secondBestNetwork = nn;
rtnn = new NeuralNetwork(nn);
}
//create new
if (breeding && bestLoss <= maxBreedingLoss)
{
if (bestNetwork != null && secondBestNetwork != null)
{
rtnn.CopyWeightsAndBiases(bestNetwork);
rtnn.Breed(secondBestNetwork);
if (lossDelta > 0.0f)
{
rtnn.Mutate(Utils.NextFloat01() * lossDelta);
}
}
else
{
rtnn.RandomizeWeightsAndBiases();
}
}
else
{
rtnn.RandomizeWeightsAndBiases();
}
}
}
else
{
rtnn.RandomizeWeightsAndBiases();
}
}
generations++;
return(rtnn);
}
