activeSegmentation.learning.WekaClassifier.java Source code

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Here is the source code for activeSegmentation.learning.WekaClassifier.java

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/*
 * 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 2
 * 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, write to the Free Software
 * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
 */
package activeSegmentation.learning;

import java.io.Serializable;
import java.util.logging.Level;
import java.util.logging.Logger;

import activeSegmentation.IClassifier;
import activeSegmentation.IDataSet;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SerializedObject;

public class WekaClassifier implements IClassifier, Serializable {

    private static final long serialVersionUID = 1765269739169476036L;

    /**
     * Classifier from Weka.
     */
    private Classifier classifier;

    public WekaClassifier(Classifier iClassifier) {
        // TODO Auto-generated constructor stub
        this.classifier = iClassifier;
    }

    /**
     * Constructs the learning model from the dataset.
     *
     * @param instances The instances to train the classifier
     * @throws Exception The exception that will be launched.
     */
    @Override
    public void buildClassifier(IDataSet instances) throws Exception {

        classifier.buildClassifier(instances.getDataset());
        System.out.println(classifier.toString());
    }

    /**
     *
     * @param instance The instance to classify.
     * @return The predicted label for the classifier.
     * @throws Exception The exception that will be launched.
     */
    @Override
    public double classifyInstance(Instance instance) throws Exception {
        return classifier.classifyInstance(instance);
    }

    /**
     * Returns the probability that has the instance to belong to each class.
     * every instance of belonging to every class that the dataset contains.
     *
     * @param instance The instance to test.
     * @return The probabilities for each class
     */
    @Override
    public double[] distributionForInstance(Instance instance) {

        try {
            return classifier.distributionForInstance(instance);
        } catch (Exception e) {
            Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, e);
        }
        return null;
    }

    /**
     * The simple name of the class.
     *
     * @return the string
     */
    @Override
    public String toString() {
        return classifier.getClass().getSimpleName();
    }

    /**
     * Set the classifier to use.
     *
     * @param classifier The weka classifier.
     */
    public void setClassifier(Classifier classifier) {
        try {
            this.classifier = weka.classifiers.AbstractClassifier.makeCopy(classifier);
        } catch (Exception e) {
            Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, e);
        }
    }

    /**
     * Evaluates the classifier using the test dataset and stores the evaluation.
     *
     * @param instances The instances to test
     * @return The evaluation
     */
    @Override
    public double[] testModel(IDataSet instances) {

        try {

            // test the current classifier with the test set
            Evaluation evaluator = new Evaluation(new Instances(instances.getDataset(), 0));

            double[] predict = evaluator.evaluateModel(classifier, instances.getDataset());

            System.out.println(evaluator.toSummaryString());
            return predict;

        } catch (Exception e) {
            Logger.getLogger(WekaClassifier.class.getName()).log(Level.SEVERE, null, e);
        }

        return null;
    }

    @Override
    public IClassifier makeCopy() throws Exception {
        // TODO Auto-generated method stub
        return (IClassifier) new SerializedObject(this).getObject();
    }

}