weka.classifiers.bayes
Class NaiveBayesSimple

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.bayes.NaiveBayesSimple

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class NaiveBayesSimple

extends Classifier

Class for building and using a simple Naive Bayes classifier. Numeric attributes are modelled by a normal distribution. For more information, see

Richard Duda and Peter Hart (1973).Pattern Classification and Scene Analysis. Wiley, New York.

Version:

$Revision: 1.12 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private double[][][]	m_Counts All the counts for nominal attributes.
private double[][]	m_Devs The standard deviations for numeric attributes.
private Instances	m_Instances The instances used for training.
private double[][]	m_Means The means for numeric attributes.
private double[]	m_Priors The prior probabilities of the classes.
private static double	NORM_CONST Constant for normal distribution.

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

NaiveBayesSimple()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String	globalInfo() Returns a string describing this classifier
static void	main(java.lang.String[] argv) Main method for testing this class.
private double	normalDens(double x, double mean, double stdDev) Density function of normal distribution.
java.lang.String	toString() Returns a description of the classifier.

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Counts

private double[][][] m_Counts

All the counts for nominal attributes.

m_Means

private double[][] m_Means

The means for numeric attributes.

m_Devs

private double[][] m_Devs

The standard deviations for numeric attributes.

m_Priors

private double[] m_Priors

The prior probabilities of the classes.

m_Instances

private Instances m_Instances

The instances used for training.

NORM_CONST

private static double NORM_CONST

Constant for normal distribution.

Constructor Detail

NaiveBayesSimple

public NaiveBayesSimple()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed

toString

public java.lang.String toString()

Returns a description of the classifier.

Returns:

a description of the classifier as a string.

normalDens

private double normalDens(double x,
                          double mean,
                          double stdDev)

Density function of normal distribution.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options