Package org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree

Class NaiveBayesSimple

java.lang.Object

org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Classifier

org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.NaiveBayesSimple

All Implemented Interfaces:

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

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: 1.13.2.2 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields

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

Fields inherited from class org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Classifier

m_Debug, m_SaAbsent, m_SaRemove

Constructor Summary

Constructors

Constructor	Description
NaiveBayesSimple()

Method Summary

Modifier and Type	Method	Description
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.
protected 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 org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.Classifier

classifyInstance, debugTipText, doDistributionForInstance, forName, getDebug, getOptions, getSaAbsent, listOptions, makeCopies, makeCopy, saAbsentTipText, setDebug, setOptions, setSaAbsent

Methods inherited from class java.lang.Object

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

Field Detail

m_Counts

protected double[][][] m_Counts

All the counts for nominal attributes.

m_Means

protected double[][] m_Means

The means for numeric attributes.

m_Devs

protected double[][] m_Devs

The standard deviations for numeric attributes.

m_Priors

protected double[] m_Priors

The prior probabilities of the classes.

m_Instances

protected Instances m_Instances

The instances used for training.

NORM_CONST

protected 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.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string.

normalDens

protected 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