public void SpawnNewCreature(Vector2 position)
{
Creature newCreature = new Creature(position, Rand.Choice(nouns), this);
creatures.Add(newCreature);
AddComponent(newCreature);
}
public NeuralNet(NeuralNet net1, NeuralNet net2, float mutationRate)
{
// Creates a network from crossing over two parent networks, with a chance of mutation
NeuralNet[] parents = { net1, net2 };
layers = new int[net1.layers.Length];
List <float[]> neuronList = new List <float[]>();
List <float[]> biasList = new List <float[]>();
List <float[][]> weightList = new List <float[][]>();
for (int i = 0; i < layers.Length; i++)
{
layers[i] = net1.layers[i];
neuronList.Add(new float[layers[i]]);
float[] bias = new float[layers[i]];
List <float[]> layerWeightList = new List <float[]>();
for (int j = 0; j < layers[i]; j++)
{
// Choose a random parent's bias
bias[j] = Rand.Choice(parents).biases[i][j];
// A small chance of slightly mutating the bias
if (Rand.Range(1f) < mutationRate)
{
bias[j] = Math.Max(-0.5f, Math.Min(0.5f, bias[j] + Rand.Range(-0.1f, 0.1f)));
}
if (i > 0)
{
float[] neuronWeights = new float[layers[i - 1]];
for (int k = 0; k < layers[i - 1]; k++)
{
// Choose a random parent's weight
neuronWeights[k] = Rand.Choice(parents).weights[i - 1][j][k];
// A small chance of slightly mutating the weight
if (Rand.Range(1f) < mutationRate)
{
neuronWeights[k] = Math.Max(-0.5f, Math.Min(0.5f, neuronWeights[k] + Rand.Range(-0.1f, 0.1f)));
}
}
layerWeightList.Add(neuronWeights);
}
}
biasList.Add(bias);
if (i > 0)
{
weightList.Add(layerWeightList.ToArray());
}
}
neurons = neuronList.ToArray();
biases = biasList.ToArray();
weights = weightList.ToArray();
}