source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Normalization.java @ 10

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;
import weka.experiment.Stats;

/**
 * Normalizes each attribute of each data set separately. 
 * @author Steffen Herbold
 */
public class Normalization implements ISetWiseProcessingStrategy, IProcessesingStrategy {

        /**
         * @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 classAtt = testdata.classAttribute();
                
                for( int i=0 ; i<testdata.numAttributes(); i++) {
                        if( !testdata.attribute(i).equals(classAtt) ) {
                                Stats teststats = testdata.attributeStats(i).numericStats;
                                
                                double minVal = teststats.min;
                                double maxVal = teststats.max;
                                
                                for( Instances traindata : traindataSet ) {
                                        Stats trainstats = traindata.attributeStats(i).numericStats;
                                        if( minVal>trainstats.min ) {
                                                minVal = trainstats.min;
                                        }
                                        if( maxVal<trainstats.max ) {
                                                maxVal = trainstats.max;
                                        }
                                }
        
                                for( int j=0 ; j<testdata.numInstances() ; j++ ) {
                                        Instance inst = testdata.instance(j);
                                        double newValue = (inst.value(i)-minVal)/(maxVal-minVal);
                                        inst.setValue(i, newValue);
                                }
                                
                                for( Instances traindata : traindataSet ) {
                                        for( int j=0 ; j<traindata.numInstances() ; j++ ) {
                                                Instance inst = traindata.instance(j);
                                                double newValue = (inst.value(i)-minVal)/(maxVal-minVal);
                                                inst.setValue(i, newValue);
                                        }
                                }
                        }
                }
                
        }
        
        /**
         * @see de.ugoe.cs.cpdp.dataprocessing.ProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
         */
        @Override
        public void apply(Instances testdata, Instances traindata) {
                final Attribute classAtt = testdata.classAttribute();
                
                for( int i=0 ; i<testdata.numAttributes(); i++) {
                        if( !testdata.attribute(i).equals(classAtt) ) {
                                Stats teststats = testdata.attributeStats(i).numericStats;
                                
                                double minVal = teststats.min;
                                double maxVal = teststats.max;
                                
                                Stats trainstats = traindata.attributeStats(i).numericStats;
                                if( minVal>trainstats.min ) {
                                        minVal = trainstats.min;
                                }
                                if( maxVal<trainstats.max ) {
                                        maxVal = trainstats.max;
                                }
        
                                for( int j=0 ; j<testdata.numInstances() ; j++ ) {
                                        Instance inst = testdata.instance(j);
                                        double newValue = (inst.value(i)-minVal)/(maxVal-minVal);
                                        inst.setValue(i, newValue);
                                }
                                
                                for( int j=0 ; j<traindata.numInstances() ; j++ ) {
                                        Instance inst = traindata.instance(j);
                                        double newValue = (inst.value(i)-minVal)/(maxVal-minVal);
                                        inst.setValue(i, newValue);
                                }
                        }
                }
        }

        /**
         * Does not have parameters. String is ignored.
         * @param parameters ignored
         */
        @Override
        public void setParameter(String parameters) {
                // no parameters
        }
}