perturbationencapsulation.java

Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant

package fasbir.ensemblers;

import weka.core.*;
import weka.classifiers.Classifier;

/**
 * <p>Description: Abstract class of perturbation methods</p>
 * A perturbation method is a meta classifier. It produces some perturbation,
 * say sample the training dataset, and train the base classifier. <br>
 * So a perturbation method should be fed another classifier. e.g.<br>
 * <code>
 *  PerturbationEncapsulation A = new ...(); <br>
 *  A.setSeed(seed); // set a randomization seed <br>
 *  A.setClassifier( another classifier ); // set base classifier<br>
 *  A.buildClassifier( dataset ); // train
 *  A.classifyInstance( data ); // test
 * </code>
 * @author Y. Yu ([e-mail]), LAMDA Group (http://lamda.nju.edu.cn/)
 * @version 1.0
 */
public abstract class PerturbationEncapsulation extends Classifier implements Randomizable{
    protected Classifier m_Classifier;
    protected int m_Seed;

    /**
     * set randomization seed
     * @param seed int
     */
    public void setSeed(int seed) {
        this.m_Seed = seed;
        if (m_Classifier instanceof Randomizable)
            ( (Randomizable) m_Classifier).setSeed(seed);
    }

    /**
     * get the randomization seed
     * @return int
     */
    public int getSeed() {
        return m_Seed;
    }

    /**
     * set the base classifier, since Perturbation_Bootstrap is a wrap of a classifier.
     * @param c Classifier
     */
    public void setClassifier(Classifier c) {
        this.m_Classifier = c;
    }

    /**
     * classify a given instance
     * @param instance Instance
     * @return double
     * @throws Exception
     */
    public double classifyInstance( Instance instance) throws Exception{
        return m_Classifier.classifyInstance(instance);
    }

    /**
     * class distribution is the same of base classifier's
     * @param instance Instance
     * @return double[]
     * @throws Exception
     */
    public double[] distributionForInstance(Instance instance) throws Exception {
        return m_Classifier.distributionForInstance( instance );
    }
}