private void initialize(String path) {
LinearLayer ILayer = new LinearLayer();
SigmoidLayer HLayer = new SigmoidLayer();
SigmoidLayer OLayer = new SigmoidLayer();
ILayer.setRows(13);
HLayer.setRows(4);
OLayer.setRows(1);
FullSynapse synIH = new FullSynapse();
FullSynapse synHO = new FullSynapse();
connect(ILayer, synIH, HLayer);
connect(HLayer, synHO, OLayer);
FileInputSynapse ITdata =
createInput(
(new StringBuilder(String.valueOf(path))).append("/wine.txt").toString(), 1, 2, 14);
FileInputSynapse IVdata =
createInput(
(new StringBuilder(String.valueOf(path))).append("/wine.txt").toString(), 131, 2, 14);
FileInputSynapse DTdata =
createInput(
(new StringBuilder(String.valueOf(path))).append("/wine.txt").toString(), 1, 1, 1);
FileInputSynapse DVdata =
createInput(
(new StringBuilder(String.valueOf(path))).append("/wine.txt").toString(), 131, 1, 1);
LearningSwitch Ilsw = createSwitch(ITdata, IVdata);
ILayer.addInputSynapse(Ilsw);
LearningSwitch Dlsw = createSwitch(DTdata, DVdata);
TeachingSynapse ts = new TeachingSynapse();
ts.setDesired(Dlsw);
OLayer.addOutputSynapse(ts);
net = new NeuralNet();
net.addLayer(ILayer, 0);
net.addLayer(HLayer, 1);
net.addLayer(OLayer, 2);
net.setTeacher(ts);
MacroPlugin mPlugin = new MacroPlugin();
String validation =
readFile(
new File(
(new StringBuilder(String.valueOf(path))).append("/validation.bsh").toString()));
mPlugin.getMacroManager().addMacro("cycleTerminated", validation);
mPlugin.setRate(100);
net.setMacroPlugin(mPlugin);
net.setScriptingEnabled(true);
Monitor mon = net.getMonitor();
mon.setLearningRate(0.20000000000000001D);
mon.setMomentum(0.29999999999999999D);
mon.setTrainingPatterns(130);
mon.setValidationPatterns(48);
mon.setTotCicles(1000);
mon.setLearning(true);
}
