// Use this for initialization
void Start()
{
Debug.Log("Population size: " + populationSize);
//int width = (int)Mathf.Round(Mathf.Sqrt(populationSize));
//int height = (int)Mathf.Round(Mathf.Sqrt(populationSize));
testing = new ComputeBuffer(10, Marshal.SizeOf(typeof(Individual)));
Debug.Log("Seed " + DateTime.Now.Millisecond);
// Fill with random genome, and run first fitness test.
int kernel = shader.FindKernel("InitializePopulation");
DebugAux.Assert(kernel >= 0, "Couldn't find kernel: " + "InitializePopulation " + kernel);
shader.SetBuffer(kernel, "Population", testing);
shader.SetFloat("seed", DateTime.Now.Millisecond);
shader.Dispatch(kernel, 32, 32, 1);
Individual[] tes = new Individual[10];
testing.GetData(tes);
for (int i = 0; i < tes.Length; i++)
{
Debug.Log(tes[i].genome + " " + tes[i].fitness);
}
// Selection..
/*kernel = shader.FindKernel("AllOnesFitness");
* DebugAux.Assert(kernel >= 0, "Couldn't find kernel: " + "AllOnesFitness " + kernel);
* shader.SetBuffer(kernel, "Population", testing);
* shader.Dispatch(kernel, 32, 32, 1);*/
testing.Dispose();
}
public static BaseGenome CreateGenome(GenomeType type, int length)
{
switch (type)
{
case GenomeType.BinaryString: return(new GenomeBinaryString(length));
case GenomeType.RawBinaryString32: return(new GenomeBinaryStringRaw32(length));
case GenomeType.RawBinaryString64: return(new GenomeBinaryStringRaw64(length));
case GenomeType.FloatString: return(new GenomeFloatString(length));
}
DebugAux.Assert(false, "There is no such genome type");
return(null);
}
public NeuralNet(int[] layerSizes, float[] initialWeights)
{
// TODO: Clear distinction between the layers.
DebugAux.Assert(layerSizes.Length >= 2, "Must have at least 2 layers");
layers = new NeuronLayer[layerSizes.Length - 1];
int counter = 0;
for (int i = 0; i < layers.Length; i++)
{
int size = (layerSizes[i] + 1) * layerSizes[i + 1];
float[] dest = new float[size];
Array.Copy(initialWeights, counter, dest, 0, size);
counter += size;
layers[i] = new NeuronLayer(layerSizes[i], layerSizes[i + 1], dest);
}
}
public override void Select(Population population, SelectionBuffer selection, GeneticAlgorithm.NextStepDelegate callback) {
int size = 0;
DebugAux.Assert(population.MaxFitness != 0, "[RouletteWheelSA] Can't have a MaxFitness of zero!");
// The algorithm states that we should pick one at random for consideration with probability 1/N.
int nrConsiderations = Mathf.RoundToInt(population.Size * Settings.SelectionProportion);
for (int i = 0; i < nrConsiderations; i++) {
int index = Random.Range(0, population.Size);
float probability = population[index].Fitness / population.MaxFitness;
if (Random.value <= probability) {
selection[size].CloneFrom(population[index].Genome);
size++;
}
}
selection.Size = size;
callback();
}
