boostingPL.boosting.AdaBoostPL.java Source code

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Here is the source code for boostingPL.boosting.AdaBoostPL.java

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/*
 *   BoostingPL - Scalable and Parallel Boosting with MapReduce 
 *   Copyright (C) 2012  Ranler Cao  findfunaax@gmail.com
 *
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.   
 */

package boostingPL.boosting;

import weka.classifiers.Classifier;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;

public class AdaBoostPL implements Classifier {

    private Classifier[][] classifiers;
    private double[] corWeights;

    public AdaBoostPL(Classifier[][] classifiers, double[][] corWeights) {
        this.classifiers = classifiers;

        this.corWeights = new double[corWeights[0].length];
        for (int i = 0; i < corWeights.length; i++) {
            for (int j = 0; j < corWeights[i].length; j++) {
                this.corWeights[j] += corWeights[i][j];
            }
            System.out.println();
        }
    }

    @Override
    public void buildClassifier(Instances insts) throws Exception {
    }

    @Override
    public double classifyInstance(Instance inst) throws Exception {
        int classNum = inst.dataset().classAttribute().numValues();
        double[] H = new double[classNum];
        for (int j = 0; j < corWeights.length; j++) {
            int classValue = merge(inst, j, classNum);
            if (classValue >= 0) {
                H[classValue] += corWeights[j];
            }

        }
        return (double) maxIdx(H);
    }

    @Override
    public double[] distributionForInstance(Instance inst) throws Exception {
        int classNum = inst.dataset().classAttribute().numValues();
        double[] H = new double[classNum];
        double sum = 0;
        for (int j = 0; j < corWeights.length; j++) {
            int classValue = merge(inst, j, classNum);
            if (classValue >= 0) {
                H[classValue] += corWeights[j];
                sum += corWeights[j];
            }
        }

        // normalize
        for (int i = 0; i < H.length; i++) {
            H[i] /= sum;
        }
        return H;
    }

    private int merge(Instance inst, int round, int classNum) throws Exception {
        int[] sum = new int[classNum];
        for (int i = 0; i < classifiers.length; i++) {
            int classIdx = (int) classifiers[i][round].classifyInstance(inst);
            sum[classIdx] += 1;
        }

        return maxIdx(sum);
    }

    private int maxIdx(int[] a) {
        int max = -1;
        int maxIdx = 0;
        for (int i = 0; i < a.length; i++) {
            if (a[i] > max) {
                maxIdx = i;
                max = a[i];
            } else if (a[i] > 0 && a[i] == max) {
                // more than two classes have same vote  
                return -1;
            }
        }
        return maxIdx;
    }

    private int maxIdx(double[] a) {
        double max = -1;
        int maxIdx = 0;
        for (int i = 0; i < a.length; i++) {
            if (a[i] > max) {
                maxIdx = i;
                max = a[i];
            } else if (a[i] > 0 && a[i] == max) {
                // more than two classes have same vote  
                return -1;
            }
        }
        return maxIdx;
    }

    @Override
    public Capabilities getCapabilities() {
        // TODO Auto-generated method stub
        return null;
    }
}