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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
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/*
 *    BinaryTreeNumericAttributeClassObserver.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Richard Kirkby ([email protected]), Andreas Hapfelmeier ([email protected])
 *
 *    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 .
 *    
 *    
 */
package moa.classifiers.core.attributeclassobservers;

import java.io.Serializable;
import moa.classifiers.core.AttributeSplitSuggestion;
import moa.classifiers.core.conditionaltests.NumericAttributeBinaryTest;
import moa.classifiers.core.splitcriteria.SplitCriterion;
import moa.core.DoubleVector;
import moa.core.ObjectRepository;
import moa.options.AbstractOptionHandler;
import moa.tasks.TaskMonitor;

/**
 * Class for observing the class data distribution for a numeric attribute using a binary tree.
 * This observer monitors the class distribution of a given attribute.
 * Used in naive Bayes and decision trees to monitor data statistics on leaves.
 *
 * @author Richard Kirkby ([email protected])
 * @version $Revision: 7 $
 */
public class BinaryTreeNumericAttributeClassObserver extends AbstractOptionHandler
        implements NumericAttributeClassObserver {

    private static final long serialVersionUID = 1L;

    public 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);
                }
            }
        }
    }

    public Node root = null;

    @Override
    public void observeAttributeClass(double attVal, int classVal, double weight) {
        if (Double.isNaN(attVal)) { //Instance.isMissingValue(attVal)
        } else {
            if (this.root == null) {
                this.root = new Node(attVal, classVal, weight);
            } else {
                this.root.insertValue(attVal, classVal, weight);
            }
        }
    }

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

    @Override
    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;
    }

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

    @Override
    protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) {
        // TODO Auto-generated method stub
    }
    
    @Override
    public void observeAttributeTarget(double attVal, double target) {
        throw new UnsupportedOperationException("Not supported yet.");
    }
   
}