wtalearningfunction.java

Kohonen network nural, framework for java developer

/**
* Copyright (c) 2006, Seweryn Habdank-Wojewodzki
* Copyright (c) 2006, Janusz Rybarski
*
* All rights reserved.
* 
* Redistribution and use in source and binary forms,
* with or without modification, are permitted provided
* that the following conditions are met:
*
* Redistributions of source code must retain the above
* copyright notice, this list of conditions and the
* following disclaimer.
*
* Redistributions in binary form must reproduce the
* above copyright notice, this list of conditions
* and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS
* AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
* THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
* WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
* OF THE POSSIBILITY OF SUCH DAMAGE.
*/

package kohonen;
import learningFactorFunctional.LearningFactorFunctionalModel;
import metrics.MetricModel;
import java.util.ArrayList;
import network.NetworkModel;
import network.NeuronModel;

/**
 * <I>Winner Takes All</I> - algorytm there only winnig neuron
 * weights are changed according to the formula w(k+1) = w(k) + n * (x-w) where <br>
 * <I>w(k+1)</I> - neuron weight in <I>k +1</I> interation <br>
 * <I>w(k)</I> - neruon weight for <I>k</I> iteration <br>
 * <I>n</I> - value of learning function factor for <I> k </I> iteriation<br>
 * <I>x</I> - learning vector od data
 * <I>w</I> - neuron weight 
 * @author Janusz Rybarski
 * e-mail: janusz.rybarski AT ae DOT krakow DOT pl
 * @author Seweryn Habdank-Wojewdzki
 * e-mail: habdank AT megapolis DOT pl
 * @version 1.0 2006/05/02
 * @see WTMLearningFunction
 */
public class WTALearningFunction {
    
    /**
     * reference to metrics
     */
    protected MetricModel metrics;
    /**
     * reference to network model
     */
    protected NetworkModel networkModel; 
    /**
     * max number of iteration
     */
    protected int maxIteration;
    /**
     * reference to learning data
     */
    protected LearningDataModel learningData;
    /**
     * reference to function model
     */
    protected LearningFactorFunctionalModel functionalModel;
    /**
     * <I>True</I> if comments during learning must be shown,<I> false</I> otherwise.
     */
    private boolean showComments;
    
    /**
     * Creates a new instance of WTALearningFunction.
     * @param networkModel network model
     * @param maxIteration iteration number
     * @param metrics metrics
     * @param learningData learnig data
     * @param functionalModel functional model
     * @see MetricModel
     * @see LearningData
     * @see NetworkModel
     * @see LearningFactorFunctionalModel
     */
    public WTALearningFunction(NetworkModel networkModel,int maxIteration,MetricModel metrics,
            LearningDataModel learningData,LearningFactorFunctionalModel functionalModel) {
        this.maxIteration = maxIteration;
        this.networkModel = networkModel;
        this.metrics = metrics;
        this.learningData = learningData;
        this.functionalModel = functionalModel;
    }

    /**
     * Return information if learning process dispalys information
     * about learning process.
     * @return true if learning process display information
     */
    public boolean isShowComments() {
        return showComments;
    }

    /**
     * Set if comments durring learning process must be shown.
     * @param showComments <B>true</B> if comments must be shown, <B>false</B> otherwise
     */
    public void setShowComments(boolean showComments) {
        this.showComments = showComments;
    }

    /**
     * Return metrics
     * @return metrics
     * @see MetricModel
     */
    public MetricModel getMetrics() {
        return metrics;
    }

    /**
     * Set metrics
     * @param metrics metrics
     */
    public void setMetrics(MetricModel metrics) {
        this.metrics = metrics;
    }

    /**
     * Set network model
     * @param networkModel network model
     */
    public void setNetworkModel(NetworkModel networkModel) {
        this.networkModel = networkModel;
    }

    /**
     * Return network model
     * @return network model
     */
    public NetworkModel getNetworkModel() {
        return networkModel;
    }

    /**
     * Set max interation
     * @param maxIteration max interation
     */
    public void setMaxIteration(int maxIteration) {
        this.maxIteration = maxIteration;
    }

    /**
     * Return maximal number of iteration
     * @return maximal number of iteration
     */
    public int getMaxIteration() {
        return maxIteration;
    }

    /**
     * Set reference to learning data
     * @param learningData reference to learning data
     */
    public void setLearningData(LearningDataModel learningData) {
        this.learningData = learningData;
    }

    /**
     * Return reference to learning data
     * @return reference to learning data
     */
    public LearningDataModel getLearningData() {
        return learningData;
    }

    /**
     * Set functional learning factor model
     * @param functionalModel functional learning factor model
     */
    public void setFunctionalModel(LearningFactorFunctionalModel functionalModel) {
        this.functionalModel = functionalModel;
    }

    /**
     * Return function model
     * @return function model
     */
    public LearningFactorFunctionalModel getFunctionalModel() {
        return functionalModel;
    }
    
    /**
     * Return number of the best neuron for specified input vector
     * @param vector input vector
     * @return Neuron number
     */
    protected int getBestNeuron(double[] vector){
        NeuronModel tempNeuron;
        double distance, bestDistance = -1;
        int networkSize = networkModel.getNumbersOfNeurons();
        int bestNeuron = 0;
        for(int i=0; i< networkSize; i++){
            tempNeuron = networkModel.getNeuron(i);
            distance = metrics.getDistance(tempNeuron.getWeight(), vector);
            if((distance < bestDistance) || (bestDistance == -1)){
                bestDistance = distance;
                bestNeuron = i;
            }
        }
        return bestNeuron;
    }
    
    /**
     * Change neuron weights for specified neuron number,iteration and input data vector
     * @param neuronNumber neuron number
     * @param vector input vector
     * @param iteration iteration number
     */
    protected void changeNeuronWeight(int neuronNumber, double[] vector, int iteration){
        double[] weightList = networkModel.getNeuron(neuronNumber).getWeight();
        int weightNumber = weightList.length;
        double weight;
                if(showComments){
            String vectorText="[";
            for(int i=0; i<vector.length; i++){
                vectorText += vector[i];
                if(i < vector.length -1 ){
                    vectorText += ", ";
                }
            }
            vectorText += "]";
            System.out.println("Vector: " + vectorText);
            String weightText="[";
            for(int i=0; i<weightList.length; i++){
                weightText += weightList[i];
                if(i < weightList.length -1 ){
                    weightText += ", ";
                }
            }
            weightText += "]";
            System.out.println("Neuron "+ (neuronNumber +1 ) + " weight before change: " + weightText);    
        }
        for (int i=0; i<weightNumber; i++){
            weight = weightList[i];
            weightList[i] += functionalModel.getValue(iteration) * (vector[i] - weight);
        }
        if(showComments){
            String weightText="[";
            for(int i=0; i<weightList.length; i++){
                weightText += weightList[i];
                if(i < weightList.length -1 ){
                    weightText += ", ";
                }
            }
            weightText += "]";
            System.out.println("Neuron "+ (neuronNumber +1 )  + " weight after change: " + weightText);
        }
    }
    
    /**
     * Start learning process
     */
    public void learn(){
        int bestNeuron = 0;
        double[] vector;
        
        int dataSize = learningData.getDataSize();
        for (int i=0; i< maxIteration; i++){
            if(showComments){
                System.out.println("Iteration number: " + (i + 1));
            }
            for(int j= 0; j<dataSize; j++){
                vector = learningData.getData(j);
                bestNeuron = getBestNeuron(vector);
                if(showComments){
                    System.out.println("Best neuron number: " + (bestNeuron + 1));
                }
                changeNeuronWeight(bestNeuron, vector, i);
            }
        }
    }
}