/// <summary>
/// The generated network.
/// </summary>
/// <returns></returns>
public BasicNetwork Generate()
{
BasicNetwork network = new BasicNetwork(new BAMLogic());
ILayer f1Layer = new BasicLayer(new ActivationBiPolar(), false,
F1Neurons);
ILayer f2Layer = new BasicLayer(new ActivationBiPolar(), false,
F2Neurons);
ISynapse synapseInputToOutput = new WeightedSynapse(f1Layer,
f2Layer);
ISynapse synapseOutputToInput = new WeightedSynapse(f2Layer,
f1Layer);
f1Layer.AddSynapse(synapseInputToOutput);
f2Layer.AddSynapse(synapseOutputToInput);
network.TagLayer(BAMPattern.TAG_F1, f1Layer);
network.TagLayer(BAMPattern.TAG_F2, f2Layer);
network.Structure.FinalizeStructure();
network.Structure.FinalizeStructure();
f1Layer.Y = PatternConst.START_Y;
f2Layer.Y = PatternConst.START_Y;
f1Layer.X = PatternConst.START_X;
f2Layer.X = PatternConst.INDENT_X;
return(network);
}
/// <summary>
/// Generate the RBF network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
int y = PatternConst.START_Y;
BasicLayer inputLayer = new BasicLayer(new ActivationLinear(),
false, this.InputNeurons);
inputLayer.X = PatternConst.START_X;
inputLayer.Y = y;
y += PatternConst.INC_Y;
BasicLayer outputLayer = new BasicLayer(ActivationFunction, false, this.OutputNeurons);
outputLayer.X = PatternConst.START_X;
outputLayer.Y = y;
NEATSynapse synapse = new NEATSynapse(inputLayer, outputLayer,
this.neurons, this.NEATActivation, 0);
synapse.Snapshot = this.Snapshot;
inputLayer.AddSynapse(synapse);
BasicNetwork network = new BasicNetwork();
network.TagLayer(BasicNetwork.TAG_INPUT, inputLayer);
network.TagLayer(BasicNetwork.TAG_OUTPUT, outputLayer);
network.Structure.FinalizeStructure();
return(network);
}
/// <summary>
/// Generate the network.
/// </summary>
/// <returns>The generated network.</returns>
public BasicNetwork Generate()
{
ILayer input, instar, outstar;
int y = PatternConst.START_Y;
BasicNetwork network = new BasicNetwork();
network.AddLayer(input = new BasicLayer(new ActivationLinear(), false, this.inputCount));
network.AddLayer(instar = new BasicLayer(new ActivationCompetitive(), false, this.instarCount));
network.AddLayer(outstar = new BasicLayer(new ActivationLinear(), false, this.outstarCount));
network.Structure.FinalizeStructure();
network.Reset();
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
instar.X = PatternConst.START_X;
instar.Y = y;
y += PatternConst.INC_Y;
outstar.X = PatternConst.START_X;
outstar.Y = y;
// tag as needed
network.TagLayer(BasicNetwork.TAG_INPUT, input);
network.TagLayer(BasicNetwork.TAG_OUTPUT, outstar);
network.TagLayer(CPNPattern.TAG_INSTAR, instar);
network.TagLayer(CPNPattern.TAG_OUTSTAR, outstar);
return(network);
}
/// <summary>
/// Generate the RBF network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
ILayer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
ILayer output = new BasicLayer(new ActivationLinear(), false, this.outputNeurons);
BasicNetwork network = new BasicNetwork();
RadialBasisFunctionLayer rbfLayer = new RadialBasisFunctionLayer(
this.hiddenNeurons);
network.AddLayer(input);
network.AddLayer(rbfLayer, SynapseType.Direct);
network.AddLayer(output);
network.Structure.FinalizeStructure();
network.Reset();
network.TagLayer(RBF_LAYER, rbfLayer);
rbfLayer.RandomizeRBFCentersAndWidths(this.inputNeurons, -1, 1, RBFEnum.Gaussian);
int y = PatternConst.START_Y;
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
rbfLayer.X = PatternConst.START_X;
rbfLayer.Y = y;
y += PatternConst.INC_Y;
output.X = PatternConst.START_X;
output.Y = y;
return(network);
}
/// <summary>
/// Generate the neural network.
/// </summary>
/// <returns>The generated neural network.</returns>
public BasicNetwork Generate()
{
BasicNetwork network = new BasicNetwork(new ART1Logic());
int y = PatternConst.START_Y;
ILayer layerF1 = new BasicLayer(new ActivationLinear(), false, this.InputNeurons);
ILayer layerF2 = new BasicLayer(new ActivationLinear(), false, this.OutputNeurons);
ISynapse synapseF1toF2 = new WeightedSynapse(layerF1, layerF2);
ISynapse synapseF2toF1 = new WeightedSynapse(layerF2, layerF1);
layerF1.Next.Add(synapseF1toF2);
layerF2.Next.Add(synapseF2toF1);
// apply tags
network.TagLayer(BasicNetwork.TAG_INPUT, layerF1);
network.TagLayer(BasicNetwork.TAG_OUTPUT, layerF2);
network.TagLayer(ART1Pattern.TAG_F1, layerF1);
network.TagLayer(ART1Pattern.TAG_F2, layerF2);
layerF1.X = PatternConst.START_X;
layerF1.Y = y;
y += PatternConst.INC_Y;
layerF2.X = PatternConst.START_X;
layerF2.Y = y;
network.SetProperty(ARTLogic.PROPERTY_A1, this.A1);
network.SetProperty(ARTLogic.PROPERTY_B1, this.B1);
network.SetProperty(ARTLogic.PROPERTY_C1, this.C1);
network.SetProperty(ARTLogic.PROPERTY_D1, this.D1);
network.SetProperty(ARTLogic.PROPERTY_L, this.L);
network.SetProperty(ARTLogic.PROPERTY_VIGILANCE, this.Vigilance);
network.Structure.FinalizeStructure();
return(network);
}
/// <summary>
/// Generate the network.
/// </summary>
/// <returns>The generated network.</returns>
public BasicNetwork Generate()
{
ILayer input, instar, outstar;
int y = PatternConst.START_Y;
BasicNetwork network = new BasicNetwork();
network.AddLayer(input = new BasicLayer(new ActivationLinear(), false, this.inputCount));
network.AddLayer(instar = new BasicLayer(new ActivationCompetitive(), false, this.instarCount));
network.AddLayer(outstar = new BasicLayer(new ActivationLinear(), false, this.outstarCount));
network.Structure.FinalizeStructure();
network.Reset();
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
instar.X = PatternConst.START_X;
instar.Y = y;
y += PatternConst.INC_Y;
outstar.X = PatternConst.START_X;
outstar.Y = y;
// tag as needed
network.TagLayer(BasicNetwork.TAG_INPUT, input);
network.TagLayer(BasicNetwork.TAG_OUTPUT, outstar);
network.TagLayer(CPNPattern.TAG_INSTAR, instar);
network.TagLayer(CPNPattern.TAG_OUTSTAR, outstar);
return network;
}
/// <summary>
/// Generate the RBF network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
int y = PatternConst.START_Y;
BasicLayer inputLayer = new BasicLayer(new ActivationLinear(),
false, this.InputNeurons);
inputLayer.X = PatternConst.START_X;
inputLayer.Y = y;
y += PatternConst.INC_Y;
BasicLayer outputLayer = new BasicLayer(ActivationFunction, false, this.OutputNeurons);
outputLayer.X = PatternConst.START_X;
outputLayer.Y = y;
NEATSynapse synapse = new NEATSynapse(inputLayer, outputLayer,
this.neurons, this.NEATActivation, 0);
synapse.Snapshot = this.Snapshot;
inputLayer.AddSynapse(synapse);
BasicNetwork network = new BasicNetwork();
network.TagLayer(BasicNetwork.TAG_INPUT, inputLayer);
network.TagLayer(BasicNetwork.TAG_OUTPUT, outputLayer);
network.Structure.FinalizeStructure();
return network;
}
/// <summary>
/// The generated network.
/// </summary>
/// <returns></returns>
public BasicNetwork Generate()
{
BasicNetwork network = new BasicNetwork(new BAMLogic());
ILayer f1Layer = new BasicLayer(new ActivationBiPolar(), false,
F1Neurons);
ILayer f2Layer = new BasicLayer(new ActivationBiPolar(), false,
F2Neurons);
ISynapse synapseInputToOutput = new WeightedSynapse(f1Layer,
f2Layer);
ISynapse synapseOutputToInput = new WeightedSynapse(f2Layer,
f1Layer);
f1Layer.AddSynapse(synapseInputToOutput);
f2Layer.AddSynapse(synapseOutputToInput);
network.TagLayer(BAMPattern.TAG_F1, f1Layer);
network.TagLayer(BAMPattern.TAG_F2, f2Layer);
network.Structure.FinalizeStructure();
network.Structure.FinalizeStructure();
f1Layer.Y = PatternConst.START_Y;
f2Layer.Y = PatternConst.START_Y;
f1Layer.X = PatternConst.START_X;
f2Layer.X = PatternConst.INDENT_X;
return network;
}
/// <summary>
/// Generate the RBF network.
/// </summary>
/// <returns>The neural network.</returns>
public BasicNetwork Generate()
{
ILayer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
ILayer output = new BasicLayer(new ActivationLinear(), false, this.outputNeurons);
BasicNetwork network = new BasicNetwork();
RadialBasisFunctionLayer rbfLayer = new RadialBasisFunctionLayer(
this.hiddenNeurons);
network.AddLayer(input);
network.AddLayer(rbfLayer, SynapseType.Direct);
network.AddLayer(output);
network.Structure.FinalizeStructure();
network.Reset();
network.TagLayer(RBF_LAYER, rbfLayer);
rbfLayer.RandomizeRBFCentersAndWidths(this.inputNeurons, -1, 1, RBFEnum.Gaussian);
int y = PatternConst.START_Y;
input.X = PatternConst.START_X;
input.Y = y;
y += PatternConst.INC_Y;
rbfLayer.X = PatternConst.START_X;
rbfLayer.Y = y;
y += PatternConst.INC_Y;
output.X = PatternConst.START_X;
output.Y = y;
return network;
}
/// <summary>
/// Generate the neural network.
/// </summary>
/// <returns>The generated neural network.</returns>
public BasicNetwork Generate()
{
BasicNetwork network = new BasicNetwork(new ART1Logic());
int y = PatternConst.START_Y;
ILayer layerF1 = new BasicLayer(new ActivationLinear(), false, this.InputNeurons);
ILayer layerF2 = new BasicLayer(new ActivationLinear(), false, this.OutputNeurons);
ISynapse synapseF1toF2 = new WeightedSynapse(layerF1, layerF2);
ISynapse synapseF2toF1 = new WeightedSynapse(layerF2, layerF1);
layerF1.Next.Add(synapseF1toF2);
layerF2.Next.Add(synapseF2toF1);
// apply tags
network.TagLayer(BasicNetwork.TAG_INPUT, layerF1);
network.TagLayer(BasicNetwork.TAG_OUTPUT, layerF2);
network.TagLayer(ART1Pattern.TAG_F1, layerF1);
network.TagLayer(ART1Pattern.TAG_F2, layerF2);
layerF1.X = PatternConst.START_X;
layerF1.Y = y;
y += PatternConst.INC_Y;
layerF2.X = PatternConst.START_X;
layerF2.Y = y;
network.SetProperty(ARTLogic.PROPERTY_A1, this.A1);
network.SetProperty(ARTLogic.PROPERTY_B1, this.B1);
network.SetProperty(ARTLogic.PROPERTY_C1, this.C1);
network.SetProperty(ARTLogic.PROPERTY_D1, this.D1);
network.SetProperty(ARTLogic.PROPERTY_L, this.L);
network.SetProperty(ARTLogic.PROPERTY_VIGILANCE, this.Vigilance);
network.Structure.FinalizeStructure();
return network;
}