source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Resampling.java @ 40

package de.ugoe.cs.cpdp.dataprocessing;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.core.Instances;
import weka.filters.Filter;
import weka.filters.supervised.instance.Resample;

/**
 * Resamples the data with WEKA {@link Resample} to have a uniform distribution among all classes.   
 * @author Steffen Herbold
 */
public class Resampling implements IProcessesingStrategy,
                ISetWiseProcessingStrategy {

        
        /**
         * Does not have parameters. String is ignored.
         * @param parameters ignored
         */
        @Override
        public void setParameter(String parameters) {
                // dummy
        }

        /*
         * (non-Javadoc)
         * @see de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
         */
        @Override
        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
                for( Instances traindata : traindataSet ) {
                        apply(testdata, traindata);
                }
        }

        /*
         * (non-Javadoc)
         * @see de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
         */
        @Override
        public void apply(Instances testdata, Instances traindata) {
                Resample resample = new Resample();
                resample.setSampleSizePercent(100);
                resample.setBiasToUniformClass(1.0);
                
                Instances traindataSample;
                try {
                        resample.setInputFormat(traindata);
                        traindataSample = Filter.useFilter(traindata, resample);
                } catch (Exception e) {
                        throw new RuntimeException(e);
                }
                traindata.clear();
                for( int i=0 ; i<traindataSample.size() ; i++ ) {
                        traindata.add(traindataSample.get(i));
                }
        }

}