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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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
* This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.
*
* For more information on Naive Bayes classifiers, see
*
* George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
*
*
* BibTeX:
*
* @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}
* }
*
*
*
* Valid options are:
*
* -K
* Use kernel density estimator rather than normal
* distribution for numeric attributes
*
* -D
* Use supervised discretization to process numeric attributes
*
*
* -O
* Display model in old format (good when there are many classes)
*
*
*
* @author Len Trigg ([email protected])
* @author Eibe Frank ([email protected])
* @version $Revision: 8034 $
*/
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: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv the options
*/
public static void main(String [] argv) {
runClassifier(new NaiveBayesUpdateable(), argv);
}
}