/**
* Generate the network.
*
* @return The generated network.
*/
public BasicNetwork generate() {
Layer input, instar, outstar;
int y = PatternConst.START_Y;
final 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.getStructure().finalizeStructure();
network.reset();
input.setX(PatternConst.START_X);
input.setY(y);
y += PatternConst.INC_Y;
instar.setX(PatternConst.START_X);
instar.setY(y);
y += PatternConst.INC_Y;
outstar.setX(PatternConst.START_X);
outstar.setY(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;
}
/**
* Generate the RSOM network.
*
* @return The neural network.
*/
public BasicNetwork generate() {
final Layer input = new BasicLayer(new ActivationLinear(), false, this.inputNeurons);
final Layer output = new BasicLayer(new ActivationLinear(), false, this.outputNeurons);
int y = PatternConst.START_Y;
final BasicNetwork network = new BasicNetwork(new SOMLogic());
network.addLayer(input);
network.addLayer(output);
input.setX(PatternConst.START_X);
output.setX(PatternConst.START_X);
input.setY(y);
y += PatternConst.INC_Y;
output.setY(y);
network.getStructure().finalizeStructure();
network.reset();
return network;
}