/*
 *    AdaHoeffdingOptionTree.java
 *    Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
 *
 *    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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package tr.gov.ulakbim.jDenetX.classifiers;

import weka.core.Instance;
import weka.core.Utils;


public class AdaHoeffdingOptionTree extends HoeffdingOptionTreeNB {

    private static final long serialVersionUID = 1L;

    public static class AdaLearningNode extends LearningNodeNB {

        private static final long serialVersionUID = 1L;

        protected double mcCorrectWeight = 0.0;

        protected double nbCorrectWeight = 0.0;

        protected double CorrectWeight = 0.0;

        protected double alpha = 0.2;

        public AdaLearningNode(double[] initialClassObservations) {
            super(initialClassObservations);
        }

        @Override
        public void learnFromInstance(Instance inst, HoeffdingOptionTree hot) {
            int trueClass = (int) inst.classValue();
            boolean blCorrect = false;
            if (this.observedClassDistribution.maxIndex() == trueClass) {
                this.mcCorrectWeight += inst.weight();
                if (this.mcCorrectWeight > this.nbCorrectWeight)
                    blCorrect = true;
            }
            if (Utils.maxIndex(NaiveBayes.doNaiveBayesPrediction(inst,
                    this.observedClassDistribution, this.attributeObservers)) == trueClass) {
                this.nbCorrectWeight += inst.weight();
                if (this.mcCorrectWeight <= this.nbCorrectWeight)
                    blCorrect = true;
            }
            if (blCorrect) {
                this.CorrectWeight += alpha * (1.0 - this.CorrectWeight); //EWMA
            } else {
                this.CorrectWeight -= alpha * this.CorrectWeight; //EWMA
            }
            super.learnFromInstance(inst, hot);
        }

        @Override
        public double[] getClassVotes(Instance inst, HoeffdingOptionTree ht) {
            double[] dist;
            if (this.mcCorrectWeight > this.nbCorrectWeight) {
                dist = this.observedClassDistribution.getArrayCopy();
            } else
                dist = NaiveBayes.doNaiveBayesPrediction(inst,
                        this.observedClassDistribution, this.attributeObservers);
            double distSum = Utils.sum(dist);
            if (distSum * (1.0 - this.CorrectWeight) * (1.0 - this.CorrectWeight) > 0.0) {
                Utils.normalize(dist, distSum * (1.0 - this.CorrectWeight) * (1.0 - this.CorrectWeight)); //Adding weight
            }
            return dist;
        }

    }

    @Override
    protected LearningNode newLearningNode(double[] initialClassObservations) {
        return new AdaLearningNode(initialClassObservations);
    }

}