public void LoadGenome(ref FileIO.GenomeData data)
{
if (data.weights.Count != 0)
{
Genome bestGenome = CreateGenomeWithoutWeights();
bestGenome.Weights = data.weights;
if (populationGenomes.Count > 0)
{
int worstGenome = 0;
float fitness = populationGenomes[0].Fitness;
for (int i = 1; i < populationGenomes.Count; i++)
{
if (populationGenomes[i].Fitness < fitness)
{
fitness = populationGenomes[i].Fitness;
worstGenome = i;
}
}
populationGenomes[worstGenome].Index = bestGenome.Index;
populationGenomes[worstGenome].Weights = bestGenome.Weights;
}
else
{
populationGenomes.Add(bestGenome);
}
}
}
void InsertBestGenomeInPopulation()
{
FileIO.GenomeData data = LoadBestGenome();
if (data != null)
{
geneticAlgorithm.LoadGenome(ref data);
}
}
void SaveBestGenome()
{
Genome bestGenome = geneticAlgorithm.GetBestGenome();
FileIO.GenomeData data = new FileIO.GenomeData(bestGenome.Weights);
string path = "BestGenomeWeights-" + data.weights.Count + "_Generation-" + geneticAlgorithm.Generation;
FileIO.WriteJson(path + ".json", ref data);
}
public Genome CreateGenomeFromData(FileIO.GenomeData data)
{
if (data.weights.Count != 0)
{
Genome bestGenome = CreateGenomeWithoutWeights();
bestGenome.Weights = data.weights;
return(bestGenome);
}
else
{
return(CreateNewGenome());
}
}
void SetupBestGenomes()
{
FileIO.GenomeData data = LoadBestGenome();
NeuralNetwork neuralNetwork = new NeuralNetwork();
if (data != null)
{
Genome genome = geneticAlgorithm.CreateGenomeFromData(LoadBestGenome());
neuralNetwork.SetFromGenomes(genome, neuralNet);
for (int i = 0; i < nnAgents.Count; i++)
{
nnAgents[i].InitializeNeuralNetwork(neuralNetwork);
}
}
else
{
for (int i = 0; i < nnAgents.Count; i++)
{
neuralNetwork.Create(neuralNet);
nnAgents[i].InitializeNeuralNetwork(neuralNetwork);
}
}
}
