/// <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);
}