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

package de.ugoe.cs.cpdp.dataprocessing;

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

import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Standardization procedure after Watanabe et al.: Adapting a Fault Prediction Model to Allow Inter Language Reuse.
 * <br><br>
 * In comparison to Watanabe et al., we transform training data instead of the test data. Otherwise, this approach would not be feasible with multiple projects.
 * @author Steffen Herbold
 */
public class AverageStandardization implements ISetWiseProcessingStrategy, IProcessesingStrategy {

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

        /**
         * @see de.ugoe.cs.cpdp.dataprocessing.SetWiseProcessingStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
         */
        @Override
        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
                final Attribute classAttribute = testdata.classAttribute();
                
                final double[] meanTest = new double[testdata.numAttributes()];
                
                // get means of testdata
                for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                        if( testdata.attribute(j)!=classAttribute ) {
                                meanTest[j] = testdata.meanOrMode(j);
                        }
                }
                
                // preprocess training data
                for( Instances traindata : traindataSet ) {
                        double[] meanTrain = new double[testdata.numAttributes()];
                        for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                                if( testdata.attribute(j)!=classAttribute ) {
                                        meanTrain[j] = traindata.meanOrMode(j);
                                }
                        }
                        
                        for( int i=0 ; i<traindata.numInstances() ; i++ ) {
                                Instance instance = traindata.instance(i);
                                for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                                        if( testdata.attribute(j)!=classAttribute ) {
                                                instance.setValue(j, instance.value(j)*meanTest[j]/meanTrain[j]);
                                        }
                                }
                        }
                }
        }

        /**
         * @see de.ugoe.cs.cpdp.dataprocessing.ProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
         */
        @Override
        public void apply(Instances testdata, Instances traindata) {
                final Attribute classAttribute = testdata.classAttribute();
                
                final double[] meanTest = new double[testdata.numAttributes()];
                
                // get means of testdata
                for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                        if( testdata.attribute(j)!=classAttribute ) {
                                meanTest[j] = testdata.meanOrMode(j);
                        }
                }
                
                // preprocess training data
                final double[] meanTrain = new double[testdata.numAttributes()];
                for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                        if( testdata.attribute(j)!=classAttribute ) {
                                meanTrain[j] = traindata.meanOrMode(j);
                        }
                }
                
                for( int i=0 ; i<traindata.numInstances() ; i++ ) {
                        Instance instance = traindata.instance(i);
                        for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
                                if( testdata.attribute(j)!=classAttribute ) {
                                        instance.setValue(j, instance.value(j)*meanTest[j]/meanTrain[j]);
                                }
                        }
                }
        }

}