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 static Func <int, double[], int[]> Get(ParentChoosingMethod method)
{
switch (method)
{
case ParentChoosingMethod.PositionLinear:
return(PositionLinear);
case ParentChoosingMethod.ScoreLinear:
return(ScoreLinear);
case ParentChoosingMethod.ScoreCubic:
return(ScoreCubic);
case ParentChoosingMethod.PositionCubic:
return(PositionCubic);
case ParentChoosingMethod.PositionExponential:
return(PositionExponential);
case ParentChoosingMethod.Geom:
return(Geom);
}
return(PositionLinear);
}
public void SetParentChoosingMethod(ParentChoosingMethod method)
{
ParentMethod = method;
Reinitialize();
}
public void SetParentChoosingMethod(ParentChoosingMethod meth)
{
parentChoosingMethod = meth;
}
