GaussianNumericAttributeClassObserver.java

/*
 *    GaussianNumericAttributeClassObserver.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.AutoExpandVector;
import tr.gov.ulakbim.jDenetX.core.DoubleVector;
import tr.gov.ulakbim.jDenetX.core.GaussianEstimator;
import weka.core.Utils;

import java.util.Set;
import java.util.TreeSet;

public class GaussianNumericAttributeClassObserver extends AbstractMOAObject
        implements AttributeClassObserver {

    private static final long serialVersionUID = 1L;

    protected int numBins;

    protected DoubleVector minValueObservedPerClass = new DoubleVector();

    protected DoubleVector maxValueObservedPerClass = new DoubleVector();

    protected AutoExpandVector<GaussianEstimator> attValDistPerClass = new AutoExpandVector<GaussianEstimator>();

    public GaussianNumericAttributeClassObserver() {
        this(10);
    }

    public GaussianNumericAttributeClassObserver(int numBins) {
        this.numBins = numBins;
    }

    public void observeAttributeClass(double attVal, int classVal, double weight) {
        if (Utils.isMissingValue(attVal)) {
        } else {
            GaussianEstimator valDist = this.attValDistPerClass.get(classVal);
            if (valDist == null) {
                valDist = new GaussianEstimator();
                this.attValDistPerClass.set(classVal, valDist);
                this.minValueObservedPerClass.setValue(classVal, attVal);
                this.maxValueObservedPerClass.setValue(classVal, attVal);
            } else {
                if (attVal < this.minValueObservedPerClass.getValue(classVal)) {
                    this.minValueObservedPerClass.setValue(classVal, attVal);
                }
                if (attVal > this.maxValueObservedPerClass.getValue(classVal)) {
                    this.maxValueObservedPerClass.setValue(classVal, attVal);
                }
            }
            valDist.addObservation(attVal, weight);
        }
    }

    public double probabilityOfAttributeValueGivenClass(double attVal,
                                                        int classVal) {
        GaussianEstimator obs = this.attValDistPerClass.get(classVal);
        return obs != null ? obs.probabilityDensity(attVal) : 0.0;
    }

    public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
            SplitCriterion criterion, double[] preSplitDist, int attIndex,
            boolean binaryOnly) {
        AttributeSplitSuggestion bestSuggestion = null;
        double[] suggestedSplitValues = getSplitPointSuggestions();
        for (double splitValue : suggestedSplitValues) {
            double[][] postSplitDists = getClassDistsResultingFromBinarySplit(splitValue);
            double merit = criterion.getMeritOfSplit(preSplitDist,
                    postSplitDists);
            if ((bestSuggestion == null) || (merit > bestSuggestion.merit)) {
                bestSuggestion = new AttributeSplitSuggestion(
                        new NumericAttributeBinaryTest(attIndex, splitValue,
                                true), postSplitDists, merit);
            }
        }
        return bestSuggestion;
    }

    public double[] getSplitPointSuggestions() {
        Set<Double> suggestedSplitValues = new TreeSet<Double>();
        double minValue = Double.POSITIVE_INFINITY;
        double maxValue = Double.NEGATIVE_INFINITY;
        for (int i = 0; i < this.minValueObservedPerClass.numValues(); i++) {
            if (this.minValueObservedPerClass.getValue(i) < minValue) {
                minValue = this.minValueObservedPerClass.getValue(i);
            }
            if (this.maxValueObservedPerClass.getValue(i) > maxValue) {
                maxValue = this.maxValueObservedPerClass.getValue(i);
            }
        }
        if (minValue < Double.POSITIVE_INFINITY) {
            double range = maxValue - minValue;
            for (int i = 0; i < this.numBins; i++) {
                double splitValue = range / (this.numBins + 1.0) * (i + 1) + minValue;
                if ((splitValue > minValue) && (splitValue < maxValue)) {
                    suggestedSplitValues.add(splitValue);
                }
            }
        }
        double[] suggestions = new double[suggestedSplitValues.size()];
        int i = 0;
        for (double suggestion : suggestedSplitValues) {
            suggestions[i++] = suggestion;
        }
        return suggestions;
    }

    // assume all values equal to splitValue go to lhs
    public double[][] getClassDistsResultingFromBinarySplit(double splitValue) {
        DoubleVector lhsDist = new DoubleVector();
        DoubleVector rhsDist = new DoubleVector();
        for (int i = 0; i < this.attValDistPerClass.size(); i++) {
            GaussianEstimator estimator = this.attValDistPerClass.get(i);
            if (estimator != null) {
                if (splitValue < this.minValueObservedPerClass.getValue(i)) {
                    rhsDist.addToValue(i, estimator.getTotalWeightObserved());
                } else if (splitValue >= this.maxValueObservedPerClass
                        .getValue(i)) {
                    lhsDist.addToValue(i, estimator.getTotalWeightObserved());
                } else {
                    double[] weightDist = estimator
                            .estimatedWeight_LessThan_EqualTo_GreaterThan_Value(splitValue);
                    lhsDist.addToValue(i, weightDist[0] + weightDist[1]);
                    rhsDist.addToValue(i, weightDist[2]);
                }
            }
        }
        return new double[][]{lhsDist.getArrayRef(), rhsDist.getArrayRef()};
    }

    public void getDescription(StringBuilder sb, int indent) { }
}