/*--- formatted by Jindent 2.1, (www.c-lab.de/~jindent) ---*//* * JOONE - Java Object Oriented Neural Engine * http://joone.sourceforge.net * * XORMemory.java * */package org.joone.samples.engine.xor;import org.joone.engine.*;import org.joone.engine.learning.*;import org.joone.io.*;import org.joone.net.*;import java.util.Vector;/** * Sample class to demostrate the use of the MemoryInputSynapse * * @author Jos??Rodriguez */public class XOR_using_NeuralNet implements NeuralNetListener {    private NeuralNet			nnet = null;    private MemoryInputSynapse  inputSynapse, desiredOutputSynapse;    private MemoryOutputSynapse outputSynapse;        // XOR input    private double[][]			inputArray = new double[][] {        {0.0, 0.0},        {0.0, 1.0},        {1.0, 0.0},        {1.0, 1.0}    };        // XOR desired output    private double[][]			desiredOutputArray = new double[][] {        {0.0},        {1.0},        {1.0},        {0.0}    };        /**     * @param args the command line arguments     */    public static void main(String args[]) {        XOR_using_NeuralNet xor = new XOR_using_NeuralNet();                xor.initNeuralNet();         xor.train();    }        /**     * Method declaration     */    public void train() {                // set the inputs        inputSynapse.setInputArray(inputArray);        inputSynapse.setAdvancedColumnSelector("1,2");                // set the desired outputs        desiredOutputSynapse.setInputArray(desiredOutputArray);        desiredOutputSynapse.setAdvancedColumnSelector("1");                // get the monitor object to train or feed forward        Monitor monitor = nnet.getMonitor();                // set the monitor parameters        monitor.setLearningRate(0.8);        monitor.setMomentum(0.3);        monitor.setTrainingPatterns(inputArray.length);        monitor.setTotCicles(1000);        monitor.setLearning(true);        nnet.addNeuralNetListener(this);        nnet.start();        nnet.getMonitor().Go();        nnet.join();        System.out.println("Network stopped");    }        /**     * Method declaration     */    protected void initNeuralNet() {                // First create the three layers        LinearLayer	input = new LinearLayer();        SigmoidLayer	hidden = new SigmoidLayer();        SigmoidLayer	output = new SigmoidLayer();                // set the dimensions of the layers        input.setRows(2);        hidden.setRows(3);        output.setRows(1);                // Now create the two Synapses        FullSynapse synapse_IH = new FullSynapse();	/* input -> hidden conn. */        FullSynapse synapse_HO = new FullSynapse();	/* hidden -> output conn. */                // Connect the input layer whit the hidden layer        input.addOutputSynapse(synapse_IH);        hidden.addInputSynapse(synapse_IH);                // Connect the hidden layer whit the output layer        hidden.addOutputSynapse(synapse_HO);        output.addInputSynapse(synapse_HO);                // the input to the neural net        inputSynapse = new MemoryInputSynapse();                input.addInputSynapse(inputSynapse);                // the output of the neural net        outputSynapse = new MemoryOutputSynapse();                output.addOutputSynapse(outputSynapse);                // The Trainer and its desired output        desiredOutputSynapse = new MemoryInputSynapse();                TeachingSynapse trainer = new TeachingSynapse();                trainer.setDesired(desiredOutputSynapse);                // Now we add this structure to a NeuralNet object        nnet = new NeuralNet();                nnet.addLayer(input, NeuralNet.INPUT_LAYER);        nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER);        nnet.addLayer(output, NeuralNet.OUTPUT_LAYER);        nnet.setTeacher(trainer);        output.addOutputSynapse(trainer);    }        public void cicleTerminated(NeuralNetEvent e) {    }        public void errorChanged(NeuralNetEvent e) {         Monitor mon = (Monitor)e.getSource();         System.out.println("Cycle: "+(mon.getTotCicles()-mon.getCurrentCicle())+" RMSE:"+mon.getGlobalError());   }        public void netStarted(NeuralNetEvent e) {    }        public void netStopped(NeuralNetEvent e) {        Monitor mon = (Monitor)e.getSource();        // Read the last pattern and print it out        Vector patts = outputSynapse.getAllPatterns();        Pattern pattern = (Pattern)patts.elementAt(patts.size() - 1);        System.out.println("Output Pattern = " + pattern.getArray()[0] + " Error: " + mon.getGlobalError());    }        public void netStoppedError(NeuralNetEvent e, String error) {    }    }/*--- formatting done in "JMRA based on Sun Java Convention" style on 05-25-2002 ---*/