public static NeuroNet Crossover(NeuroNet mother, NeuroNet father)
{
NeuroNet child = InitChild(mother, father);
for (int i = 0; i < child.Weights.Length; i++)
{
for (int j = 0; j < child.Weights[i].Length; j++)
{
for (int k = 0; k < child.Weights[i][j].Length; k++)
{
if (!(k >= mother.Weights[i][j].Length && mother.Weights[i][j].Length < father.Weights[i][j].Length) &&
((k >= father.Weights[i][j].Length && mother.Weights[i][j].Length > father.Weights[i][j].Length) || NeuroHelper.RandomNext(0, 100) > 50))
{
child.Weights[i][j][k] = mother.Weights[i][j][k];
}
else
{
child.Weights[i][j][k] = father.Weights[i][j][k];
}
}
if (!(j >= mother.Biases[i].Length && mother.Biases[i].Length < father.Biases[i].Length) &&
((j >= father.Biases[i].Length && mother.Biases[i].Length > father.Biases[i].Length) || NeuroHelper.RandomNext(0, 100) > 50))
{
child.Biases[i][j] = mother.Biases[i][j];
}
else
{
child.Biases[i][j] = father.Biases[i][j];
}
}
}
return(child);
}
public NeuroNet(NeuroNet copy)
{
this.layers = new int[copy.layers.Length];
for (int i = 0; i < this.layers.Length; i++)
{
this.layers[i] = copy.layers[i];
}
this.InitNeurons();
this.InitWeightsAndBiases(copy.Weights, copy.Biases);
}
public static void Save(NeuroNet neuroNet, string fileName)
{
Genome genome = new Genome(neuroNet);
string stringifiedGenome = "";
stringifiedGenome += genome.LayerCount + seperator.ToString();
stringifiedGenome += genome.NeuronsPerLayer.ToString('_') + seperator;
stringifiedGenome += genome.Weights.ToString('_') + seperator;
stringifiedGenome += genome.Biases.ToString('_') + seperator;
using (StreamWriter streamWriter = new StreamWriter(SAVE_DIRECTORY + fileName + ".genome"))
{
streamWriter.Write(stringifiedGenome);
streamWriter.Flush();
streamWriter.Close();
}
}
public Genome(NeuroNet neuroNet)
{
this.LayerCount = neuroNet.LayerCount;
int[] neuronsPerLayer = new int[LayerCount];
for (int i = 0; i < neuronsPerLayer.Length; i++)
{
neuronsPerLayer[i] = neuroNet.Neurons[i].Length;
}
this.NeuronsPerLayer = neuronsPerLayer;
List <float> Weights = new List <float>();
for (int i = 1; i < this.LayerCount; i++)
{
int index = i - 1;
for (int j = 0; j < neuroNet.Weights[index].Length; j++)
{
for (int k = 0; k < neuroNet.Weights[index][j].Length; k++)
{
Weights.Add(neuroNet.Weights[index][j][k]);
}
}
}
this.Weights = Weights.ToArray();
List <float> Biases = new List <float>();
for (int i = 1; i < this.LayerCount; i++)
{
int index = i - 1;
for (int j = 0; j < neuroNet.Biases[index].Length; j++)
{
Biases.Add(neuroNet.Biases[index][j]);
}
}
this.Biases = Biases.ToArray();
}
private static NeuroNet InitChild(NeuroNet mother, NeuroNet father)
{
int[] fetchedLayers = new int[Math.Max(mother.layers.Length, father.layers.Length)];
for (int i = 0; i < fetchedLayers.Length; i++)
{
if (mother.layers[i] >= father.layers[i])
{
fetchedLayers[i] = mother.layers[i];
}
else
{
fetchedLayers[i] = father.layers[i];
}
}
NeuroNet child = new NeuroNet
{
layers = fetchedLayers
};
child.InitNeurons();
child.InitWeightsAndBiases();
return(child);
}
