/* * ValidationSample.java * * Created on 11 november 2002, 22.59 * @author  pmarrone */package org.joone.samples.engine.validation;import org.joone.engine.*;import org.joone.engine.learning.*;import org.joone.net.*;import org.joone.io.*;import org.joone.util.*;/** * This example shows how to check the training level of the net * using a validation data source. * In this example we will learn to use the following objects: * - org.joone.util.LearningSwitch * - org.joone.net.NeuralNetValidator * - org.joone.util.NormalizerPlugIn * * This program shows how to build the same kind of neural net as that * contained into the org/joone/samples/editor/scripting/ValidationSample.ser * file using only java code and the core engine's API. Open that net in * the GUI editor to see the architecture of the net built in this example. */public class SimpleValidationSample implements NeuralNetListener, NeuralValidationListener {        NeuralNet net;    /** Creates a new instance of SampleScript */    public SimpleValidationSample() {    }        /**     * @param args the command line arguments     */    public static void main(String[] args) {        if (args.length == 0) {            System.out.println("Usage: java org.joone.samples.engine.validation.SimpleValidationSample filePath");            System.out.println("where filePath is the path containing the input file");            System.exit(1);        }        SimpleValidationSample sampleNet = new SimpleValidationSample();        sampleNet.initialize(args[0]);        sampleNet.start();    }        private void initialize(String path) {        /* Creates the three layers and connect them */        LinearLayer ILayer = new LinearLayer();  // Input Layer        SigmoidLayer HLayer = new SigmoidLayer(); // Hidden Layer        SigmoidLayer OLayer = new SigmoidLayer(); // Output Layer        ILayer.setRows(13); // The input pattern has 13 columns        HLayer.setRows(10);        OLayer.setRows(1);  // The desired pattern has 1 column        FullSynapse synIH = new FullSynapse();        FullSynapse synHO = new FullSynapse();        this.connect(ILayer, synIH, HLayer);        this.connect(HLayer, synHO, OLayer);                /* Creates all the required input data sets */        FileInputSynapse ITdata = this.createInput(path+"/wine.txt",1,2,14); /* The input training data set */        FileInputSynapse IVdata = this.createInput(path+"/wine.txt",131,2,14); /* The input validation data set */        FileInputSynapse DTdata = this.createInput(path+"/wine.txt",1,1,1); /* The desired training data set */        FileInputSynapse DVdata = this.createInput(path+"/wine.txt",131,1,1); /* The desired validation data set */                /* Creates and attach the input learning switch */        LearningSwitch Ilsw = this.createSwitch(ITdata, IVdata);        ILayer.addInputSynapse(Ilsw);                /* Creates and attach the desired learning switch */        LearningSwitch Dlsw = this.createSwitch(DTdata, DVdata);        TeachingSynapse ts = new TeachingSynapse(); // The teacher of the net        ts.setDesired(Dlsw);        OLayer.addOutputSynapse(ts);                /* Now we put all togheter into a NeuralNet object */        net = new NeuralNet();        net.addLayer(ILayer, NeuralNet.INPUT_LAYER);        net.addLayer(HLayer, NeuralNet.HIDDEN_LAYER);        net.addLayer(OLayer, NeuralNet.OUTPUT_LAYER);        net.setTeacher(ts);                /* Sets the Monitor's parameters */        Monitor mon = net.getMonitor();        mon.setLearningRate(0.6);        mon.setMomentum(0.5);                mon.setTrainingPatterns(130);        mon.setValidationPatterns(48);                mon.setTotCicles(1000);        mon.setLearning(true);    }        /** Creates a FileInputSynapse */    private FileInputSynapse createInput(String name, int firstRow, int firstCol, int lastCol) {        FileInputSynapse input = new FileInputSynapse();        input.setFileName(name);        input.setFirstRow(firstRow);        if (firstCol != lastCol)            input.setAdvancedColumnSelector(firstCol+"-"+lastCol);        else            input.setAdvancedColumnSelector(Integer.toString(firstCol));                // We normalize the input data in the range 0 - 1        NormalizerPlugIn norm = new NormalizerPlugIn();        if (firstCol != lastCol)            norm.setAdvancedSerieSelector("1-"+Integer.toString(lastCol-firstCol+1));        else            norm.setAdvancedSerieSelector("1");        input.setPlugin(norm);        return input;    }        /** Connects two Layers with a Synapse */    private void connect(Layer ly1, Synapse syn, Layer ly2) {        ly1.addOutputSynapse(syn);        ly2.addInputSynapse(syn);    }        /* Creates a LearningSwitch and attach to it both the training and       the desired input synapses */    private LearningSwitch createSwitch(StreamInputSynapse IT, StreamInputSynapse IV) {        LearningSwitch lsw = new LearningSwitch();        lsw.addTrainingSet(IT);        lsw.addValidationSet(IV);        return lsw;    }        private void start() {        // Registers itself as a listener        net.getMonitor().addNeuralNetListener(this);        net.start();        net.getMonitor().Go();    }        /* Events */    public void netValidated(NeuralValidationEvent event) {        // Shows the RMSE at the end of the cycle        NeuralNet NN = (NeuralNet)event.getSource();        System.out.println("    Validation Error: "+NN.getMonitor().getGlobalError());    }        public void cicleTerminated(NeuralNetEvent e) {        // Prints out the cycle and the training error        int cycle = net.getMonitor().getTotCicles() - net.getMonitor().getCurrentCicle()+1;        if (cycle % 200 == 0) { // We validate the net every 200 cycles            System.out.println("Cycle #"+cycle);            System.out.println("    Training Error:   " + net.getMonitor().getGlobalError());                        // Creates a copy of the neural network            net.getMonitor().setExporting(true);            NeuralNet newNet = net.cloneNet();            net.getMonitor().setExporting(false);                        // Cleans the old listeners            // This is a fundamental action to avoid that the validating net            // calls the cicleTerminated method of this class            newNet.removeAllListeners();                        // Set all the parameters for the validation            NeuralNetValidator nnv = new NeuralNetValidator(newNet);            nnv.addValidationListener(this);            nnv.start();  // Validates the net        }    }        public void errorChanged(NeuralNetEvent e) {//        Monitor NN = (Monitor)e.getSource();//        System.out.println("    Actual training error: "+NN.getGlobalError());    }        public void netStarted(NeuralNetEvent e) {    }        public void netStopped(NeuralNetEvent e) {    }        public void netStoppedError(NeuralNetEvent e,String error) {    }    }