public void FillUpRestSpots(List <NeuralNetwork2D> betterNetworks)
{
while (betterNetworks.Count < populationCount)
{
NeuralNetwork2D newNetwork = playerController2D.gameObject.AddComponent <NeuralNetwork2D>();
newNetwork.Initialise(playerController2D.layers, playerController2D.neurons);
betterNetworks.Add(newNetwork);
}
}
public void CreatePopulation()
{
while (startIndex < populationCount)
{
NeuralNetwork2D network = playerController2D.gameObject.AddComponent <NeuralNetwork2D>();
network.Initialise(playerController2D.layers, playerController2D.neurons);
networks.Add(network);
startIndex++;
}
startIndex = 0;
text1.text = "Generation: " + currentGeneration;
text2.text = "Genome: " + currentGenome;
playerController2D.network = networks[currentGenome];
}
public void CrossOver(List <NeuralNetwork2D> betterNet)
{
for (int i = 0; i < numberToCrossover - 1; i += 2)
{
NeuralNetwork2D Child1 = playerController2D.gameObject.AddComponent <NeuralNetwork2D>();
NeuralNetwork2D Child2 = playerController2D.gameObject.AddComponent <NeuralNetwork2D>();
Child1.Initialise(playerController2D.layers, playerController2D.neurons);
Child2.Initialise(playerController2D.layers, playerController2D.neurons);
for (int j = 0; j < betterNet[i].weights.Count; j++)
{
if (Random.Range(0, 2) == 0)
{
Child1.weights[j] = betterNet[i].weights[j];
Child2.weights[j] = betterNet[i + 1].weights[j];
}
else
{
Child2.weights[j] = betterNet[i].weights[j];
Child1.weights[j] = betterNet[i + 1].weights[j];
}
}
for (int j = 0; j < betterNet[i].biases.Count; j++)
{
if (Random.Range(0, 2) == 0)
{
Child1.biases[j] = betterNet[i].biases[j];
Child2.biases[j] = betterNet[i + 1].biases[j];
}
else
{
Child2.biases[j] = betterNet[i].biases[j];
Child1.biases[j] = betterNet[i + 1].biases[j];
}
}
betterNet.Add(Child1);
betterNet.Add(Child2);
}
}
public NeuralNetwork2D InitialiseCopy(int hiddenLayerCount, int hiddenNeuronCount)
{
NeuralNetwork2D n = FindObjectOfType <PlayerController2D>().gameObject.AddComponent <NeuralNetwork2D>();
List <Matrix <float> > newWeights = new List <Matrix <float> >();
for (int i = 0; i < this.weights.Count; i++)
{
Matrix <float> currentWeight = Matrix <float> .Build.Dense(weights[i].RowCount, weights[i].ColumnCount);
for (int x = 0; x < currentWeight.RowCount; x++)
{
for (int y = 0; y < currentWeight.ColumnCount; y++)
{
currentWeight[x, y] = weights[i][x, y];
}
}
newWeights.Add(currentWeight);
}
List <float> newBiases = new List <float>();
newBiases.AddRange(biases);
n.weights = newWeights;
n.biases = newBiases;
for (int i = 0; i < hiddenLayerCount + 1; i++)
{
Matrix <float> newHiddenLayer = Matrix <float> .Build.Dense(1, hiddenNeuronCount);
n.hiddenLayers.Add(newHiddenLayer);
}
n.fitness = 0;
return(n);
}
