public FoldingWorkingSet(string Name, bool Hamming, GeneticAlgorithmConfig <char> GAC, Func <GeneticAlgorithm <char>, CalculationMode <char> > CM) :
base(Name, GAC, new FoldingDefaultOperatorProvider(), new FoldingCreator(), CM)
{
this.CalculateHamming = Hamming;
this.Log = new ConsoleLogger();
//this.Log = new StreamLogger(string.Format("{0}-{1}-{2}.log", Name, GAC.PopulationSize, DateTime.Now.ToString("yyyy-MM-dd-HH-mm")));
}
public SilentWorkingSet(string Name,
GeneticAlgorithmConfig <char> GAC,
IGeneticOperatorProvider <char> Provider,
Func <GeneticAlgorithm <char>, CalculationMode <char> > CM) :
base(Name, GAC, Provider, new FoldingCreator(), CM)
{
this.Log = new StreamLogger(string.Format("{0}-{1}-{2}-{3}-{4}.log", Name, GAC.PopulationSize, GAC.CrossoverRate.ToString("F2"), GAC.MutationRate.ToString("F2"), DateTime.Now.ToString("yyyy-MM-dd-HH-mm")));;
}
protected WorkingSet(string Name, GeneticAlgorithmConfig <T> GAC, IGeneticOperatorProvider <T> Provider, IFitnessMeasuredCreator <T> Creator, Func <GeneticAlgorithm <T>, C> CM)
{
this.Lock = new ManualResetEvent(false);
this.Name = Name;
this.Log = new EmptyLogger();
//this.Log = new StreamLogger(string.Format("{0}-{1}-{2}.log", Name, GAC.PopulationSize, DateTime.Now.ToString("yyyy-MM-dd-HH-mm")));
this.GA = new GeneticAlgorithm <T>(GAC, Provider, Creator, Log);
this.CalculationMode = CM(this.GA);
this.GAC = GAC;
}
void Awake()
{
config = new GeneticAlgorithmConfig();
config.RandOptions = new RandomizerOptions(-1, 1);
config.PercentToSelect = PercentToSelect;
config.MutationChance = MutationChance;
//config.ParentChances = Chances;
config.SetParentChoosingMethod(parentChoosingMethod);
Application.runInBackground = true;
}
public GeneticAlgorithmConfig GetGeneticAlgorithmConfig()
{
var result = new GeneticAlgorithmConfig()
{
MutationChance = 1,
ParentMethod = ParentChoosingMethod.Geom,
PercentToSelect = 2,
RandOptions = GetRandomizerOptions()
};
return(result);
}
public Classification()
{
GeneticAlgorithmConfig config = new GeneticAlgorithmConfig();
config.RandOptions = new RandomizerOptions(-1, 1, 0.2);
config.PercentToSelect = 0.5;
config.MutationChance = 0.2;
config.SetParentChoosingMethod(ParentChoosingMethod.PositionLinear);
process = new LearningProcess(populationCount, config, new List <int> {
2, 4, 2
}, new List <Type>()
{
typeof(TanHNeuron), typeof(StepNeuron)
});
}
public Perceptron()
{
GeneticAlgorithmConfig config = new GeneticAlgorithmConfig();
config.RandOptions = new RandomizerOptions(-1, 1, 0.05);
config.PercentToSelect = 0.4;
config.MutationChance = 0.005;
config.SetParentChoosingMethod(ParentChoosingMethod.Geom);
process = new LearningProcess(populationCount, config, new List <int> {
3, 100, 2
}, new List <Type>()
{
typeof(IdentityNeuron), typeof(IdentityNeuron)
});
}
public LearningProcess LoadLearningProcess(string path)
{
if (path == null || !File.Exists(path))
{
return(null);
}
learningProcess = Serializator.Deserialize(path);
if (learningProcess == null)
{
throw new Exception("Błąd deserializacji!!!");
}
config = learningProcess.LearningAlgorithm.Config;
parentChoosingMethod = config.ParentMethod;
sigma = (float)config.RandOptions.Sigma;
PercentToSelect = (float)config.PercentToSelect;
MutationChance = (float)config.MutationChance;
List <Type> neuronTypes = new List <Type>();
foreach (LayerBase <double> neuron in learningProcess.Population[0].Layers)
{
neuronTypes.Add(neuron.Neurons[0].GetType());
}
Debug.Log(learningProcess.Population[0].Layers[0].Neurons[0]);
neuronDefinitionsPanelScript.
SetNeuronDefinitions(
learningProcess.Population[0].LayersCount,
neuronTypes
);
//plot.PreviousLearningProcess(learningProcess);
plot.RestartedSimulation(learningProcess);
ProcessData[] histData = new ProcessData[learningProcess.HistoricalData.Count];
learningProcess.HistoricalData.CopyTo(histData);
GenerationList.SetGenerationHistory(new List <ProcessData>(histData));
return(learningProcess);
}
public IFitnessMeasured <char> CreateNew(GeneticAlgorithmConfig <char> GAC)
{
return(new Folding(GAC.Sequence.Length - 1));
}
