/*
* GreenwaldKhannaNumericAttributeClassObserver.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.GreenwaldKhannaQuantileSummary;
import weka.core.Utils;
public class GreenwaldKhannaNumericAttributeClassObserver extends
AbstractMOAObject implements AttributeClassObserver {
private static final long serialVersionUID = 1L;
protected int numTuples;
protected AutoExpandVector<GreenwaldKhannaQuantileSummary> attValDistPerClass = new AutoExpandVector<GreenwaldKhannaQuantileSummary>();
public GreenwaldKhannaNumericAttributeClassObserver(int numTuples) {
this.numTuples = numTuples;
}
public void observeAttributeClass(double attVal, int classVal, double weight) {
if (Utils.isMissingValue(attVal)) {
} else {
GreenwaldKhannaQuantileSummary valDist = this.attValDistPerClass
.get(classVal);
if (valDist == null) {
valDist = new GreenwaldKhannaQuantileSummary(this.numTuples);
this.attValDistPerClass.set(classVal, valDist);
}
// TODO: not taking weight into account
valDist.insert(attVal);
}
}
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) {
AttributeSplitSuggestion bestSuggestion = null;
for (GreenwaldKhannaQuantileSummary qs : this.attValDistPerClass) {
if (qs != null) {
double[] cutpoints = qs.getSuggestedCutpoints();
for (double cutpoint : cutpoints) {
double[][] postSplitDists = getClassDistsResultingFromBinarySplit(cutpoint);
double merit = criterion.getMeritOfSplit(preSplitDist,
postSplitDists);
if ((bestSuggestion == null)
|| (merit > bestSuggestion.merit)) {
bestSuggestion = new AttributeSplitSuggestion(
new NumericAttributeBinaryTest(attIndex,
cutpoint, true), postSplitDists, merit);
}
}
}
}
return bestSuggestion;
}
// 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++) {
GreenwaldKhannaQuantileSummary estimator = this.attValDistPerClass
.get(i);
if (estimator != null) {
long countBelow = estimator.getCountBelow(splitValue);
lhsDist.addToValue(i, countBelow);
rhsDist.addToValue(i, estimator.getTotalCount() - countBelow);
}
}
return new double[][]{lhsDist.getArrayRef(), rhsDist.getArrayRef()};
}
public void getDescription(StringBuilder sb, int indent) {
// TODO Auto-generated method stub
}
}