BinaryTreeNumericAttributeClassObserver.java

/*
 *    BinaryTreeNumericAttributeClassObserver.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 *
 *    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.attributes;

import tr.gov.ulakbim.jDenetX.AbstractMOAObject;
import tr.gov.ulakbim.jDenetX.classifiers.splits.SplitCriterion;
import tr.gov.ulakbim.jDenetX.core.DoubleVector;

import java.io.Serializable;

public class BinaryTreeNumericAttributeClassObserver extends AbstractMOAObject
        implements AttributeClassObserver {

    private static final long serialVersionUID = 1L;

    protected class Node implements Serializable {

        private static final long serialVersionUID = 1L;

        public double cut_point;

        public DoubleVector classCountsLeft = new DoubleVector();

        public DoubleVector classCountsRight = new DoubleVector();

        public Node left;

        public Node right;

        public Node(double val, int label, double weight) {
            this.cut_point = val;
            this.classCountsLeft.addToValue(label, weight);
        }

        public void insertValue(double val, int label, double weight) {
            if (val == this.cut_point) {
                this.classCountsLeft.addToValue(label, weight);
            } else if (val <= this.cut_point) {
                this.classCountsLeft.addToValue(label, weight);
                if (this.left == null) {
                    this.left = new Node(val, label, weight);
                } else {
                    this.left.insertValue(val, label, weight);
                }
            } else { // val > cut_point
                this.classCountsRight.addToValue(label, weight);
                if (this.right == null) {
                    this.right = new Node(val, label, weight);
                } else {
                    this.right.insertValue(val, label, weight);
                }
            }
        }
    }

    protected Node root = null;

    public void observeAttributeClass(double attVal, int classVal, double weight) {
        if (Double.isNaN(attVal)) {

        } else {
            if (this.root == null) {
                this.root = new Node(attVal, classVal, weight);
            } else {
                this.root.insertValue(attVal, classVal, weight);
            }
        }
    }

    public double probabilityOfAttributeValueGivenClass(double attVal,
                                                        int classVal) {
        // TODO: NaiveBayes broken until implemented
        return 0.0;
    }

    public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
            SplitCriterion criterion, double[] preSplitDist, int attIndex,
            boolean binaryOnly) {
        return searchForBestSplitOption(this.root, null, null, null, null, false,
                criterion, preSplitDist, attIndex);
    }

    protected AttributeSplitSuggestion searchForBestSplitOption(
            Node currentNode, AttributeSplitSuggestion currentBestOption,
            double[] actualParentLeft,
            double[] parentLeft, double[] parentRight, boolean leftChild,
            SplitCriterion criterion, double[] preSplitDist, int attIndex) {
        if (currentNode == null) {
            return currentBestOption;
        }
        DoubleVector leftDist = new DoubleVector();
        DoubleVector rightDist = new DoubleVector();
        if (parentLeft == null) {
            leftDist.addValues(currentNode.classCountsLeft);
            rightDist.addValues(currentNode.classCountsRight);
        } else {
            leftDist.addValues(parentLeft);
            rightDist.addValues(parentRight);
            if (leftChild) {
                //get the exact statistics of the parent value
                DoubleVector exactParentDist = new DoubleVector();
                exactParentDist.addValues(actualParentLeft);
                exactParentDist.subtractValues(currentNode.classCountsLeft);
                exactParentDist.subtractValues(currentNode.classCountsRight);

                // move the subtrees
                leftDist.subtractValues(currentNode.classCountsRight);
                rightDist.addValues(currentNode.classCountsRight);

                // move the exact value from the parent
                rightDist.addValues(exactParentDist);
                leftDist.subtractValues(exactParentDist);

            } else {
                leftDist.addValues(currentNode.classCountsLeft);
                rightDist.subtractValues(currentNode.classCountsLeft);
            }
        }
        double[][] postSplitDists = new double[][]{leftDist.getArrayRef(),
                rightDist.getArrayRef()};
        double merit = criterion.getMeritOfSplit(preSplitDist, postSplitDists);
        if ((currentBestOption == null) || (merit > currentBestOption.merit)) {
            currentBestOption = new AttributeSplitSuggestion(
                    new NumericAttributeBinaryTest(attIndex,
                            currentNode.cut_point, true), postSplitDists, merit);

        }
        currentBestOption = searchForBestSplitOption(currentNode.left,
                currentBestOption, currentNode.classCountsLeft.getArrayRef(), postSplitDists[0], postSplitDists[1], true,
                criterion, preSplitDist, attIndex);
        currentBestOption = searchForBestSplitOption(currentNode.right,
                currentBestOption, currentNode.classCountsLeft.getArrayRef(), postSplitDists[0], postSplitDists[1], false,
                criterion, preSplitDist, attIndex);
        return currentBestOption;
    }

    public void getDescription(StringBuilder sb, int indent) {
        // TODO Auto-generated method stub

    }

}