/*
* $RCSfile: KnnNumericDecisionDependentProbability.java,v $
* $Revision: 1.3 $
* $Date: 2007/06/30 17:30:32 $
* $Author: wojna $
*
* Copyright (C) 2002 - 2007 Logic Group, Institute of Mathematics, Warsaw University
*
* This file is part of Rseslib.
*
* Rseslib 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.
*
* Rseslib 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 <http://www.gnu.org/licenses/>.
*/
package rseslib.processing.classification.parameterised.knn;
import java.util.Properties;
import rseslib.processing.metrics.MetricFactory;
import rseslib.structure.attribute.ArrayHeader;
import rseslib.structure.attribute.Attribute;
import rseslib.structure.data.DoubleData;
import rseslib.structure.data.DoubleDataObject;
import rseslib.structure.data.DoubleDataWithDecision;
import rseslib.structure.probability.DecisionDependentProbability;
import rseslib.structure.table.ArrayListDoubleDataTable;
import rseslib.structure.table.DoubleDataTable;
import rseslib.system.Configuration;
import rseslib.system.PropertyConfigurationException;
import rseslib.system.progress.EmptyProgress;
/**
* @author Łukasz Ligowski
*/
public class KnnNumericDecisionDependentProbability extends Configuration implements DecisionDependentProbability
{
private ArrayHeader newHeader;
private KnnClassifier m_Classifier;
/**
* @param prop
* @param table
* @param index
*/
public KnnNumericDecisionDependentProbability(Properties prop,DoubleDataTable table,int index) throws PropertyConfigurationException, InterruptedException
{
super(prop);
Attribute[] newAttrList = new Attribute[2];
newAttrList[0]=table.attributes().attribute(index);
newAttrList[1]=table.attributes().nominalDecisionAttribute();
newHeader = new ArrayHeader(newAttrList, table.attributes().missing());
DoubleDataObject container;
ArrayListDoubleDataTable transformedTable = new ArrayListDoubleDataTable(newHeader);
for (DoubleData temp : table.getDataObjects())
{
container = new DoubleDataObject(newHeader);
container.set(0,temp.get(index));
container.setDecision(((DoubleDataWithDecision)temp).getDecision());
transformedTable.add(container);
}
Properties knnProp = Configuration.loadDefaultProperties(KnnClassifier.class);
knnProp.setProperty(MetricFactory.METRIC_PROPERTY_NAME, getProperty(MetricFactory.METRIC_PROPERTY_NAME));
knnProp.setProperty(MetricFactory.VICINITY_SIZE_FOR_DBVDM_PROPERTY_NAME, getProperty(MetricFactory.VICINITY_SIZE_FOR_DBVDM_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.WEIGHTING_METHOD_PROPERTY_NAME, getProperty(KnnClassifier.WEIGHTING_METHOD_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.LEARN_OPTIMAL_K_PROPERTY_NAME, getProperty(KnnClassifier.LEARN_OPTIMAL_K_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.MAXIMAL_K_PROPERTY_NAME, getProperty(KnnClassifier.MAXIMAL_K_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.K_PROPERTY_NAME, getProperty(KnnClassifier.K_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.FILTER_NEIGHBOURS_PROPERTY_NAME, getProperty(KnnClassifier.FILTER_NEIGHBOURS_PROPERTY_NAME));
knnProp.setProperty(KnnClassifier.VOTING_PROPERTY_NAME, getProperty(KnnClassifier.VOTING_PROPERTY_NAME));
m_Classifier = new KnnClassifier(knnProp,transformedTable,new EmptyProgress());
}
public double getProbability(double value, int decision)
{
DoubleDataObject container = new DoubleDataObject(newHeader);
container.set(0,value);
try
{
double[] decDistr = m_Classifier.classifyWithDistributedDecision(container);
double sum = 0;
for (int dec = 0; dec < decDistr.length; dec++)
sum += decDistr[dec];
return decDistr[decision]/sum;
}
catch (PropertyConfigurationException e)
{
return 0;
}
}
}
