public void CreateGenomes(int populationSize)
{
if (GenomeFactory == null)
{
throw new ApplicationException("Impossível criar genomas no algoritimo genético com o GenomeFactory nulo!");
}
this.populationSize = populationSize;
Genome[] genomes = new Genome[populationSize];
for (int i = 0; i < populationSize; i++)
{
gnomes.Add(GenomeFactory.CreateGenome(this));
}
}
// Adaptive GAs
// In CAGA (clustering-based adaptive genetic algorithm)
void Start()
{
settings = GetComponent <GASettings>();
populationSize = settings.PopulationSize;
nrGenerations = settings.NumberOfGenerations;
blocking = settings.RunAsFastAsPossible;
environment.Settings = settings;
selectionStrategy.Settings = settings;
matingStrategy.Settings = settings;
mutationStrategy.Settings = settings;
population = new Population(populationSize);
selectionBuffer = new SelectionBuffer(populationSize);
statistics = GetComponent <Statistics>();
if (statistics == null)
{
statistics = gameObject.AddComponent <Statistics>();
}
statistics.Population = population;
BaseGenome genomeTemplate = GenomeFactory.CreateGenome(genomeType, genomeSize);
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
population[i] = new PhenomeDescription(genome);
}
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
selectionBuffer[i] = genome;
}
nextStepDelegate = NextStep;
currentState = State.FitnessTest;
environment.Inititalize(population);
}
public AllOnesPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.RawBinaryString64, 64);
}
public CarPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.FloatString, (5 * 8 + 8) + (8 * 3 + 3));
}
public CardPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.RawBinaryString32, 10);
}
void generateSampleCPPNs()
{
NeatParameters np = new NeatParameters();
IdGenerator idGen = new IdGenerator();
idGen.ResetNextInnovationNumber();
Random r = new Random();
JObject root = new JObject();
JObject meta = new JObject();
JArray networkArray = new JArray();
//how many random input tests?
int testCount = 100;
int networkCount = 20;
meta.Add("networkCount", networkCount);
meta.Add("sampleCount", testCount);
Console.WriteLine("All Networks start will run:" + networkCount * testCount);
for (int n = 0; n < networkCount; n++)
{
Console.WriteLine("Network start:" + n);
JObject networkJSON = new JObject();
//create our genome
var inputCount = r.Next(4) + 3;
var outputCount = r.Next(3) + 1;
//radnom inputs 3-6, random outputs 1-3
var genome = GenomeFactory.CreateGenome(np, idGen, inputCount, outputCount, 1);
Console.WriteLine("Genoem created:" + n);
Hashtable nodeHT = new Hashtable();
Hashtable connHT = new Hashtable();
//mutate our genome
for (int m = 0; m < 20; m++)
{
((NeatGenome)genome).Mutate(np, idGen, nodeHT, connHT);
Console.WriteLine("Mutation done: " + m);
}
Console.WriteLine("Mutations done:" + n);
//now turn genome into a network
var network = genome.Decode(null);
Console.WriteLine("genome decoded:" + n);
//turn network into JSON, and save as the network object
networkJSON.Add("network", JObject.FromObject(network));
JArray inputsAndOutputs = new JArray();
Console.WriteLine("starting tests:" + n);
for (var t = 0; t < testCount; t++)
{
Console.WriteLine("Test " + t + " :" + "for" + n);
JArray inputSamples = new JArray();
JArray outputSamples = new JArray();
network.ClearSignals();
Console.WriteLine("Testclear " + t + " :" + "for" + n);
//var saveInputs = new float[inputCount];
for (int ins = 0; ins < inputCount; ins++)
{
//inputs from -1,1
var inF = (float)(2 * r.NextDouble() - 1);
//saveInputs[ins] = inF;
network.SetInputSignal(ins, inF);
//add our random input
inputSamples.Add(JToken.FromObject(inF));
}
Console.WriteLine("Testrecursive next" + t + " :" + "for" + n);
//run our network, and save the response
((ModularNetwork)network).RecursiveActivation();
//network.MultipleSteps(30);
Console.WriteLine("recursive done " + t + " :" + "for" + n);
//var saveOuts = new float[outputCount];
for (var outs = 0; outs < outputCount; outs++)
{
//saveOuts[outs] = network.GetOutputSignal(outs);
//keep our outputs in an output array
outputSamples.Add(JToken.FromObject(network.GetOutputSignal(outs)));
}
//network.ClearSignals();
//network.SetInputSignals(saveInputs);
//network.MultipleSteps(30);
////((ModularNetwork)network).RecursiveActivation();
//for (var outs = 0; outs < outputCount; outs++)
//{
// Console.WriteLine("Difference in activation: " + Math.Abs(network.GetOutputSignal(outs) - saveOuts[outs]));
//}
Console.WriteLine("test reached past outputs " + t + " :" + "for" + n);
JObject test = new JObject();
test.Add("inputs", inputSamples);
test.Add("outputs", outputSamples);
inputsAndOutputs.Add(test);
Console.WriteLine("Ins/outs done " + t + " :" + "for" + n);
}
Console.WriteLine("tests ended:" + n);
//we add our inputs/outs for this json network
networkJSON.Add("tests", inputsAndOutputs);
//finally, we add our network json to the network array
networkArray.Add(networkJSON);
Console.WriteLine("Network finished:" + n);
}
Console.WriteLine("All newtorks finished, cleaning up");
//add our networks, and add our meta information
root.Add("networks", networkArray);
root.Add("meta", meta);
//and away we go! Let's save to file!
using (System.IO.StreamWriter file = new System.IO.StreamWriter("testgenomes.json"))
{
file.WriteLine(root.ToString());
}
}
