// Copyright 2015 Georg-August-Universität Göttingen, Germany // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. package de.ugoe.cs.cpdp.dataprocessing; import de.ugoe.cs.cpdp.util.WekaUtils; import de.ugoe.cs.cpdp.util.WekaUtils.DistChar; import weka.core.Instances; /** *
* Normalization selected according to the TCA+ rules after Nam et al. (Transfer Defect Learning). *
* * @author Steffen Herbold */ public class TCAPlusNormalization implements IProcessesingStrategy { /** * Does not have parameters. String is ignored. * * @param parameters * ignored */ @Override public void setParameter(String parameters) { // dummy, paramters not used } /* * (non-Javadoc) * * @see de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.Instances, * weka.core.Instances) */ @Override public void apply(Instances testdata, Instances traindata) { applyTCAPlus(testdata, traindata); } private void applyTCAPlus(Instances testdata, Instances traindata) { DistChar dcTest = WekaUtils.datasetDistance(testdata); DistChar dcTrain = WekaUtils.datasetDistance(traindata); // RULE 1: if (0.9 * dcTrain.mean <= dcTest.mean && 1.1 * dcTrain.mean >= dcTest.mean && 0.9 * dcTrain.std <= dcTest.std && 1.1 * dcTrain.std >= dcTest.std) { // do nothing } // RULE 2: else if ((0.4 * dcTrain.min > dcTest.min || 1.6 * dcTrain.min < dcTest.min) && (0.4 * dcTrain.max > dcTest.max || 1.6 * dcTrain.min < dcTest.max) && (0.4 * dcTrain.min > dcTest.num || 1.6 * dcTrain.min < dcTest.num)) { NormalizationUtil.minMax(testdata); NormalizationUtil.minMax(traindata); } // RULE 3: else if ((0.4 * dcTrain.std > dcTest.std && dcTrain.num < dcTest.num) || (1.6 * dcTrain.std < dcTest.std) && dcTrain.num > dcTest.num) { NormalizationUtil.zScoreTraining(testdata, traindata); } // RULE 4: else if ((0.4 * dcTrain.std > dcTest.std && dcTrain.num > dcTest.num) || (1.6 * dcTrain.std < dcTest.std) && dcTrain.num < dcTest.num) { NormalizationUtil.zScoreTarget(testdata, traindata); } // RULE 5: else { NormalizationUtil.zScore(testdata); NormalizationUtil.zScore(traindata); } } }