private void CBAktywacje_SelectionChanged(object sender, System.Windows.Controls.SelectionChangedEventArgs e)
{
ComboBoxItem typeItem = (ComboBoxItem)CBAktywacje.SelectedItem;
string value = typeItem.Content.ToString();
switch (value)
{
case "Linear":
ActivationFunction = new ActivationLinear();
break;
case "LOG":
ActivationFunction = new ActivationLOG();
break;
case "Sigmoid":
ActivationFunction = new ActivationSigmoid();
break;
case "SIN":
ActivationFunction = new ActivationSIN();
break;
case "TANH":
ActivationFunction = new ActivationTANH();
break;
}
}
public IActivationFunction GetActivationFunction()
{
IActivationFunction activation;
switch (ActivationFunction)
{
case ActivationFunctionType.Linear:
activation = new ActivationLinear();
break;
case ActivationFunctionType.Sigmoid:
activation = new ActivationSigmoid();
break;
case ActivationFunctionType.TanH:
activation = new ActivationTANH();
break;
case ActivationFunctionType.SoftMax:
activation = new ActivationSoftMax();
break;
case ActivationFunctionType.ReLU:
activation = new ActivationReLU();
break;
default:
throw new ArgumentOutOfRangeException();
}
return(activation);
}
/// <summary>
/// Setup for training.
/// </summary>
private void Init()
{
// default values
ParamActivationMutationRate = 0.1;
ParamChanceAddLink = 0.07;
ParamChanceAddNode = 0.04;
ParamChanceAddRecurrentLink = 0.05;
ParamCompatibilityThreshold = 0.26;
ParamCrossoverRate = 0.7;
ParamMaxActivationPerturbation = 0.1;
ParamMaxNumberOfSpecies = 0;
ParamMaxPermittedNeurons = 100;
ParamMaxWeightPerturbation = 0.5;
ParamMutationRate = 0.2;
ParamNumAddLinkAttempts = 5;
ParamNumGensAllowedNoImprovement = 15;
ParamNumTrysToFindLoopedLink = 5;
ParamNumTrysToFindOldLink = 5;
ParamProbabilityWeightReplaced = 0.1;
NeatActivationFunction = new ActivationSigmoid();
OutputActivationFunction = new ActivationLinear();
//
NEATGenome genome = (NEATGenome)Population.Genomes[0];
Population.Innovations =
new NEATInnovationList(Population, genome.Links,
genome.Neurons);
splits = Split(null, 0, 1, 0);
if (CalculateScore.ShouldMinimize)
{
bestEverScore = double.MaxValue;
}
else
{
bestEverScore = double.MinValue;
}
ResetAndKill();
SortAndRecord();
SpeciateAndCalculateSpawnLevels();
}
public static FeedforwardNetwork CreateNetwork()
{
ActivationFunction threshold = new ActivationSigmoid();
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(threshold, Config.INPUT_SIZE));
network.AddLayer(new FeedforwardLayer(threshold,
Config.NEURONS_HIDDEN_1));
if (Config.NEURONS_HIDDEN_2 > 0)
{
network.AddLayer(new FeedforwardLayer(threshold,
Config.NEURONS_HIDDEN_2));
}
network.AddLayer(new FeedforwardLayer(threshold, Config.OUTPUT_SIZE));
network.Reset();
return(network);
}
/// <summary>
/// Creates a feedforward NN
/// </summary>
public virtual void createNetwork()
{
IActivationFunction threshold;
if (ACTIVIATION_FUNCTION == 1)
threshold = new ActivationSigmoid();
else if (ACTIVIATION_FUNCTION == 2)
threshold = new ActivationTANH();
else
throw new System.Exception("Only 2 activation functions have been impletemented.");
network = new BasicNetwork();
network.AddLayer(new BasicLayer(threshold, true, INPUT_NEURONS));
network.AddLayer(new BasicLayer(threshold, true, HIDDENLAYER1_NEURONS));
if (HIDDENLAYER2_NEURONS > 0)
{
network.AddLayer(new BasicLayer(threshold, true, HIDDENLAYER2_NEURONS));
}
network.AddLayer(new BasicLayer(threshold, true, OUTPUT_NEURONS));
network.Structure.FinalizeStructure();
network.Reset();
}
void AddLayers(List <LayerConfig> gen)
{
foreach (var g in gen)
{
IActivationFunction act;
if (g.ActivationType == 0)
{
act = new ActivationBiPolar();
}
switch (g.ActivationType)
{
case 0:
act = new ActivationBiPolar();
break;
case 1:
act = new ActivationBipolarSteepenedSigmoid();
break;
case 2:
act = new ActivationClippedLinear();
break;
case 3:
act = new ActivationCompetitive();
break;
case 4:
act = new ActivationElliott();
break;
case 5:
act = new ActivationElliottSymmetric();
break;
case 6:
act = new ActivationGaussian();
break;
case 7:
act = new ActivationLinear();
break;
case 8:
act = new ActivationLOG();
break;
case 9:
act = new ActivationRamp();
break;
case 10:
act = new ActivationRamp();
break;
case 11:
act = new ActivationSigmoid();
break;
case 12:
act = new ActivationSIN();
break;
case 13:
act = new ActivationSoftMax();
break;
case 14:
act = new ActivationSteepenedSigmoid();
break;
case 15:
act = new ActivationStep();
break;
case 16:
act = new ActivationTANH();
break;
default:
act = new ActivationSoftMax();
break;
}
network.AddLayer(new BasicLayer(act, g.hasBias, g.neurons));
}
}
private NEATNetwork Create()
{
IList <NEATNeuron> neurons = new List <NEATNeuron>();
IActivationFunction afSigmoid = new ActivationSigmoid();
IActivationFunction afStep = new ActivationStep();
// create the neurons
NEATNeuron input1 = new NEATNeuron(
NEATNeuronType.Input,
1,
0.1,
0.2,
0.3);
NEATNeuron input2 = new NEATNeuron(
NEATNeuronType.Input,
2,
0.1,
0.2,
0.3);
NEATNeuron bias = new NEATNeuron(
NEATNeuronType.Bias,
3,
0.1,
0.2,
0.3);
NEATNeuron hidden1 = new NEATNeuron(
NEATNeuronType.Hidden,
4,
0.1,
0.2,
0.3);
NEATNeuron output = new NEATNeuron(
NEATNeuronType.Output,
5,
0.1,
0.2,
0.3);
// add the neurons
neurons.Add(input1);
neurons.Add(input2);
neurons.Add(hidden1);
neurons.Add(bias);
neurons.Add(output);
// connect everything
Link(0.01, input1, hidden1, false);
Link(0.01, input2, hidden1, false);
Link(0.01, bias, hidden1, false);
Link(0.01, hidden1, output, false);
// create the network
NEATNetwork result = new NEATNetwork(2,
1,
neurons,
afSigmoid,
afStep,
3);
return(result);
}
