com.daniel.convert.IncrementalClassifier.java Source code

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Here is the source code for com.daniel.convert.IncrementalClassifier.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., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    IncrementalClassifier.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *
 */

package com.daniel.convert;

import java.io.File;
import java.util.Random;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.BayesNet;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffLoader;

/**
 * This example trains NaiveBayes incrementally on data obtained from the
 * ArffLoader.
 * 
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5628 $
 */
public class IncrementalClassifier {

    /**
     * Expects an ARFF file as first argument (class attribute is assumed to be
     * the last attribute).
     * 
     * @param args
     *            the commandline arguments
     * @throws Exception
     *             if something goes wrong
     */
    public static BayesNet treinar(String[] args) throws Exception {
        // load data
        ArffLoader loader = new ArffLoader();
        loader.setFile(new File(args[0]));
        Instances structure = loader.getStructure();
        structure.setClassIndex(structure.numAttributes() - 1);

        // train NaiveBayes
        BayesNet BayesNet = new BayesNet();

        Instance current;
        while ((current = loader.getNextInstance(structure)) != null) {
            structure.add(current);
        }
        BayesNet.buildClassifier(structure);

        // output generated model
        // System.out.println(nb);

        // test set
        BayesNet BayesNetTest = new BayesNet();

        // test the model
        Evaluation eTest = new Evaluation(structure);
        // eTest.evaluateModel(nb, structure);
        eTest.crossValidateModel(BayesNetTest, structure, 15, new Random(1));

        // Print the result  la Weka explorer:
        String strSummary = eTest.toSummaryString();
        System.out.println(strSummary);

        return BayesNet;
    }
}