source: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/WekaLASERTraining.java @ 95

// 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.training;

import java.util.LinkedList;
import java.util.List;

import de.ugoe.cs.cpdp.util.WekaUtils;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.Instance;
import weka.core.Instances;


/**
 * <p>
 * TODO comment
 * </p>
 * 
 * @author Steffen Herbold
 */
public class WekaLASERTraining extends WekaBaseTraining implements ITrainingStrategy {

    private final LASERClassifier internalClassifier = new LASERClassifier();

    @Override
    public Classifier getClassifier() {
        return internalClassifier;
    }

    @Override
    public void apply(Instances traindata) {
        try {
            internalClassifier.buildClassifier(traindata);
        }
        catch (Exception e) {
            throw new RuntimeException(e);
        }
    }

    public class LASERClassifier extends AbstractClassifier {

        private static final long serialVersionUID = 1L;
        
        private Classifier laserClassifier = null;
        private Instances traindata = null;

        @Override
        public double classifyInstance(Instance instance) throws Exception {
            List<Integer> closestInstances = new LinkedList<>();
            double minDistance = Double.MAX_VALUE;
            for( int i=0; i<traindata.size(); i++ ) {
                double distance = WekaUtils.hammingDistance(instance, traindata.get(i));
                if( distance<minDistance) {
                    minDistance = distance;
                }
            }
            for( int i=0; i<traindata.size(); i++ ) {
                double distance = WekaUtils.hammingDistance(instance, traindata.get(i));
                if( distance<=minDistance ) {
                    closestInstances.add(i);
                }
            }
            if( closestInstances.size()==1 ) {
                int closestIndex = closestInstances.get(0);
                Instance closestTrainingInstance = traindata.get(closestIndex);
                List<Integer> closestToTrainingInstance = new LinkedList<>();
                double minTrainingDistance = Double.MAX_VALUE;
                for( int i=0; i<traindata.size(); i++ ) {
                    if( closestIndex!=i ) {
                        double distance = WekaUtils.hammingDistance(closestTrainingInstance, traindata.get(i));
                        if( distance<minTrainingDistance ) {
                            minTrainingDistance = distance;
                        }
                    }
                }
                for( int i=0; i<traindata.size(); i++ ) {
                    if( closestIndex!=i ) {
                        double distance = WekaUtils.hammingDistance(closestTrainingInstance, traindata.get(i));
                        if( distance<=minTrainingDistance ) {
                            closestToTrainingInstance.add(i);
                        }
                    }
                }
                if( closestToTrainingInstance.size()==1 ) {
                    return laserClassifier.classifyInstance(instance);
                }
                else {
                    double label = Double.NaN;
                    boolean allEqual = true;
                    for( Integer index : closestToTrainingInstance ) {
                        if( Double.isNaN(label) ) {
                            label = traindata.get(index).classValue();
                        }
                        else if( label!=traindata.get(index).classValue() ) {
                            allEqual = false;
                            break;
                        }
                    }
                    if( allEqual ) {
                        return label;
                    }
                    else {
                        return laserClassifier.classifyInstance(instance);
                    }
                }
            } else {
                double label = Double.NaN;
                boolean allEqual = true;
                for( Integer index : closestInstances ) {
                    if( Double.isNaN(label) ) {
                        label = traindata.get(index).classValue();
                    }
                    else if( label!=traindata.get(index).classValue() ) {
                        allEqual = false;
                        break;
                    }
                }
                if( allEqual ) {
                    return label;
                }
                else {
                    return laserClassifier.classifyInstance(instance);
                }
            }
        }

        @Override
        public void buildClassifier(Instances traindata) throws Exception {
            this.traindata = new Instances(traindata);
            laserClassifier = setupClassifier();
            laserClassifier.buildClassifier(traindata);
        }
    }
}