public NetworkSaveState[] breed(NetworkSaveState parent1, NetworkSaveState parent2, int children)
{
NetworkSaveState[] offspring = new NetworkSaveState[children];
for (int childIndex = 0; childIndex < children; childIndex++)
{
NetworkSaveState child = parent1.deepCopy();
for (int i = 0; i < parent2.weights.Count; i++)
{
if (Utils.chance(Settings.crossoverFactor))
{
child.weights[i] = parent2.weights[i];
}
}
for (int i = 0; i < child.weights.Count; i++)
{
if (Utils.chance(Settings.mutationRate))
{
child.weights[i] += Utils.rangeAroundZero(Settings.mutationMax);
}
}
offspring[childIndex] = child;
}
return(offspring);
}
public void loadState(NetworkSaveState save)
{
int prevNeurons = 0;
int index = 0;
int indexWeights = 0;
layers = new Layer[save.neuronsPerLayer.Count];
for (int i = 0; i < save.neuronsPerLayer.Count; i++)
{
Layer layer = new Layer(index, save.neuronsPerLayer[i], prevNeurons);
for (int j = 0; j < layer.neurons.Length; j++)
{
for (int k = 0; k < layer.neurons[j].weights.Length; k++)
{
layer.neurons[j].weights[k] = save.weights[indexWeights];
indexWeights++;
}
}
prevNeurons = save.neuronsPerLayer[i];
index++;
layers[i] = layer;
}
}
public NetworkSaveState[] firstGenerationNetworks()
{
NetworkSaveState[] gen = new NetworkSaveState[Settings.populationSize];
for (int i = 0; i < Settings.populationSize; i++)
{
Network neuralNet = new Network(Settings.inputNeurons, Settings.hiddenNeurons, Settings.ouputNeurons);
gen[i] = neuralNet.saveState();
}
generations.Add(new Generation());
return(gen);
}
public NetworkSaveState saveState()
{
NetworkSaveState save = new NetworkSaveState();
for (int i = 0; i < layers.Length; i++)
{
save.neuronsPerLayer.Add(layers[i].neurons.Length);
for (int j = 0; j < layers[i].neurons.Length; j++)
{
for (int k = 0; k < layers[i].neurons[j].weights.Length; k++)
{
save.weights.Add(layers[i].neurons[j].weights[k]);
}
}
}
return(save);
}
