weka.classifiers.bayes.NaiveBayesUpdateable.java Source code

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Here is the source code for weka.classifiers.bayes.NaiveBayesUpdateable.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 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/>.
 */

/*
 *    NaiveBayesUpdateable.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.bayes;

import weka.classifiers.UpdateableClassifier;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;

/**
 <!-- globalinfo-start -->
 * Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.<br/>
 * This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.<br/>
 * <br/>
 * For more information on Naive Bayes classifiers, see<br/>
 * <br/>
 * George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{John1995,
 *    address = {San Mateo},
 *    author = {George H. John and Pat Langley},
 *    booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence},
 *    pages = {338-345},
 *    publisher = {Morgan Kaufmann},
 *    title = {Estimating Continuous Distributions in Bayesian Classifiers},
 *    year = {1995}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -K
 *  Use kernel density estimator rather than normal
 *  distribution for numeric attributes</pre>
 * 
 * <pre> -D
 *  Use supervised discretization to process numeric attributes
 * </pre>
 * 
 * <pre> -O
 *  Display model in old format (good when there are many classes)
 * </pre>
 * 
 <!-- options-end -->
 *
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class NaiveBayesUpdateable extends NaiveBayes implements UpdateableClassifier {

    /** for serialization */
    static final long serialVersionUID = -5354015843807192221L;

    /**
     * Returns a string describing this classifier
     * @return a description of the classifier suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
        return "Class for a Naive Bayes classifier using estimator classes. This is the "
                + "updateable version of NaiveBayes.\n"
                + "This classifier will use a default precision of 0.1 for numeric attributes "
                + "when buildClassifier is called with zero training instances.\n\n"
                + "For more information on Naive Bayes classifiers, see\n\n" + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing 
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        return super.getTechnicalInformation();
    }

    /**
     * Set whether supervised discretization is to be used.
     *
     * @param newblah true if supervised discretization is to be used.
     */
    public void setUseSupervisedDiscretization(boolean newblah) {

        if (newblah) {
            throw new IllegalArgumentException("Can't use discretization " + "in NaiveBayesUpdateable!");
        }
        m_UseDiscretization = false;
    }

    /**
     * Returns the revision string.
     * 
     * @return      the revision
     */
    public String getRevision() {
        return RevisionUtils.extract("$Revision$");
    }

    /**
     * Main method for testing this class.
     *
     * @param argv the options
     */
    public static void main(String[] argv) {
        runClassifier(new NaiveBayesUpdateable(), argv);
    }
}