/*--- 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.*;/** * Sample class to demostrate the use of the MemoryInputSynapse * * @author Jos??Rodriguez */public class XORMemory implements NeuralNetListener {        // XOR input    private double[][]  inputArray = new double[][] {        {0.0, 0.0, 0.0},        {0.0, 1.0, 1.0},        {1.0, 0.0, 1.0},        {1.0, 1.0, 0.0}    };        private long mills;    /**     * @param args the command line arguments     */    public static void main(String args[]) {        XORMemory   xor = new XORMemory();                xor.Go();    }        /**     * Method declaration     */    public void Go() {        // Firts, creates the three Layers        SigmoidLayer	input = new SigmoidLayer();        SigmoidLayer	hidden = new SigmoidLayer();        SigmoidLayer	output = new SigmoidLayer();                input.setLayerName("input");        hidden.setLayerName("hidden");        output.setLayerName("output");                // sets their dimensions        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. */                synapse_IH.setName("IH");        synapse_HO.setName("HO");                // 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);                // Create the Monitor object and set the learning parameters        Monitor monitor = new Monitor();                monitor.setLearningRate(0.8);        monitor.setMomentum(0.3);                // Passe the Monitor to all components        input.setMonitor(monitor);        hidden.setMonitor(monitor);        output.setMonitor(monitor);                // The application registers itself as monitor's listener so it can receive        // the notifications of termination from the net.        monitor.addNeuralNetListener(this);                MemoryInputSynapse  inputStream = new MemoryInputSynapse();                // The first two columns contain the input values        inputStream.setInputArray(inputArray);        inputStream.setAdvancedColumnSelector("1,2");                // set the input data        input.addInputSynapse(inputStream);                TeachingSynapse trainer = new TeachingSynapse();                trainer.setMonitor(monitor);                // Setting of the file containing the desired responses provided by a FileInputSynapse        MemoryInputSynapse samples = new MemoryInputSynapse();                        // The output values are on the third column of the file        samples.setInputArray(inputArray);        samples.setAdvancedColumnSelector("3");        trainer.setDesired(samples);                // Connects the Teacher to the last layer of the net        output.addOutputSynapse(trainer);                /*         * All the layers must be activated invoking their method start;         * the layers are implemented as Runnable objects, then they are         * instanziated on separated threads.         */        input.start();        hidden.start();        output.start();        monitor.setTrainingPatterns(4);	// # of rows (patterns) contained in the input file        monitor.setTotCicles(10000);		// How many times the net must be trained on the input patterns        monitor.setLearning(true);		// The net must be trained        mills = System.currentTimeMillis();        monitor.Go();					// The net starts the training job    }        /**     * Method declaration     */    public void netStopped(NeuralNetEvent e) {        long delay = System.currentTimeMillis() - mills;        System.out.println("Training finished after "+delay+" ms");        System.exit(0);    }        /**     * Method declaration     */    public void cicleTerminated(NeuralNetEvent e) {    }        /**     * Method declaration     */    public void netStarted(NeuralNetEvent e) {        System.out.println("Training...");    }        public void errorChanged(NeuralNetEvent e) {        Monitor mon = (Monitor) e.getSource();        long	c = mon.getCurrentCicle();        long	cl = c / 1000;                // We want to print the results every 1000 cycles        if ((cl * 1000) == c) {            System.out.println(c + " cycles remaining - Error = " + mon.getGlobalError());        }    }        public void netStoppedError(NeuralNetEvent e,String error) {    }    }/*--- formatting done in "JMRA based on Sun Java Convention" style on 05-08-2002 ---*/