org.vimarsha.classifier.impl.TimeslicedClassifier.java Source code

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Here is the source code for org.vimarsha.classifier.impl.TimeslicedClassifier.java

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/*
 * vimarsha, Performance analysis: Machine Learning Approach
 * Copyright (C) 2013 vimarsha
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see <http://www.gnu.org/licenses/>.
 */

package org.vimarsha.classifier.impl;

import org.vimarsha.exceptions.ClassificationFailedException;
import weka.classifiers.meta.FilteredClassifier;
import weka.classifiers.trees.J48;
import weka.filters.unsupervised.attribute.Remove;

import java.util.LinkedList;

/**
 * Created with IntelliJ IDEA.
 * User: sunimal
 */
public class TimeslicedClassifier extends AbstractClassifier {

    private LinkedList<String> output;

    public TimeslicedClassifier() {
        super();
    }

    /**
     * Classifies Timesliced test data instances.
     *
     * @return Resulting linked list with timelsiced classification results.
     * @throws ClassificationFailedException
     */
    @Override
    public Object classify() throws ClassificationFailedException {
        output = new LinkedList<String>();
        J48 j48 = new J48();
        Remove rm = new Remove();
        rm.setAttributeIndices("1");
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);
        fc.setClassifier(j48);
        try {
            fc.buildClassifier(trainSet);

            for (int i = 0; i < testSet.numInstances(); i++) {
                //System.out.println(testSet.instance(i));
                double pred = fc.classifyInstance(testSet.instance(i));
                output.add(testSet.classAttribute().value((int) pred));
            }
        } catch (Exception ex) {
            System.out.println(ex.toString());
            throw new ClassificationFailedException();
        }
        return output;
    }

    /**
     * Get the resulting LinkedList of a previously evaluated dataset.
     *
     * @return LinkedList containing the instance labels and the associated classification results.
     */
    @Override
    public Object getClassificationResult() {
        return output;
    }
}