source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TCAPlusNormalization.java @ 135

// 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;

/**
 * <p>
 * Normalization selected according to the TCA+ rules after Nam et al. (Transfer Defect Learning).
 * </p>
 * 
 * @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);
        }
    }
}