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();
}
public Creature(Vector2 _position, string _name, Simulation _sim, Color _color, NeuralNet _network, int _generation) : base(_position)
{
// Constructs a creature using given network, colour, and generation
name = _name;
generation = _generation;
color = _color;
sim = _sim;
network = new NeuralNet(13, sim.settings.hiddenLayerCount, sim.settings.hiddenLayerSize, 4);
energy = sim.settings.maxEnergy / 2;
scale = 0.1f;
rotation = Rand.Range(360);
}
public Creature(Vector2 _position, string _name, Simulation _sim) : base(_position)
{
// Constructs a creature using a random network, colour, and starting at generation 0
name = _name;
generation = 0;
color = Rand.RandColor();
sim = _sim;
network = new NeuralNet(13, sim.settings.hiddenLayerCount, sim.settings.hiddenLayerSize, 4);
energy = sim.settings.maxEnergy / 2;
scale = 0.1f;
rotation = Rand.Range(360);
}
public CreatureSave(Creature creature)
{
name = creature.name;
network = creature.network;
x = creature.position.X;
y = creature.position.Y;
childrenCount = creature.childrenCount;
generation = creature.generation;
age = creature.age;
reproductionTimer = creature.reproductionTimer;
energy = creature.energy;
colorValue = creature.color.PackedValue;
rotation = creature.rotation;
scale = creature.scale;
}