weka.classifiers.bayes
Class NaiveBayes

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

All Implemented Interfaces:

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

Direct Known Subclasses:

NaiveBayesUpdateable

public class NaiveBayes

extends Classifier

implements OptionHandler, WeightedInstancesHandler

Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable 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 and Pat Langley (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp. 338-345. Morgan Kaufmann, San Mateo.

Valid options are:

-K
Use kernel estimation for modelling numeric attributes rather than a single normal distribution.

-D
Use supervised discretization to process numeric attributes.

Version:

$Revision: 1.15 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

protected static double	DEFAULT_NUM_PRECISION The precision parameter used for numeric attributes
protected Estimator	m_ClassDistribution The class estimator.
protected Discretize	m_Disc The discretization filter.
protected Estimator[][]	m_Distributions The attribute estimators.
protected Instances	m_Instances The dataset header for the purposes of printing out a semi-intelligible model
protected int	m_NumClasses The number of classes (or 1 for numeric class)
protected boolean	m_UseDiscretization Whether to use discretization than normal distribution for numeric attributes
protected boolean	m_UseKernelEstimator Whether to use kernel density estimator rather than normal distribution for numeric attributes

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

NaiveBayes()

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[]	getOptions() Gets the current settings of the classifier.
boolean	getUseKernelEstimator() Gets if kernel estimator is being used.
boolean	getUseSupervisedDiscretization() Get whether supervised discretization is to be used.
java.lang.String	globalInfo() Returns a string describing this classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseKernelEstimator(boolean v) Sets if kernel estimator is to be used.
void	setUseSupervisedDiscretization(boolean newblah) Set whether supervised discretization is to be used.
java.lang.String	toString() Returns a description of the classifier.
void	updateClassifier(Instance instance) Updates the classifier with the given instance.
java.lang.String	useKernelEstimatorTipText() Returns the tip text for this property
java.lang.String	useSupervisedDiscretizationTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_Distributions

protected Estimator[][] m_Distributions

The attribute estimators.

m_ClassDistribution

protected Estimator m_ClassDistribution

The class estimator.

m_UseKernelEstimator

protected boolean m_UseKernelEstimator

Whether to use kernel density estimator rather than normal distribution for numeric attributes

m_UseDiscretization

protected boolean m_UseDiscretization

Whether to use discretization than normal distribution for numeric attributes

m_NumClasses

protected int m_NumClasses

The number of classes (or 1 for numeric class)

m_Instances

protected Instances m_Instances

The dataset header for the purposes of printing out a semi-intelligible model

DEFAULT_NUM_PRECISION

protected static final double DEFAULT_NUM_PRECISION

The precision parameter used for numeric attributes

See Also:

Constant Field Values

m_Disc

protected Discretize m_Disc

The discretization filter.

Constructor Detail

NaiveBayes

public NaiveBayes()

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

updateClassifier

public void updateClassifier(Instance instance)
                      throws java.lang.Exception

Updates the classifier with the given instance.

Parameters:

instance - the new training instance to include in the model

Throws:

java.lang.Exception - if the instance could not be incorporated in the model.

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 there is a problem generating the prediction

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-K
Use kernel estimation for modelling numeric attributes rather than a single normal distribution.

-D
Use supervised discretization to process numeric attributes.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

toString

public java.lang.String toString()

Returns a description of the classifier.

Returns:

a description of the classifier as a string.

useKernelEstimatorTipText

public java.lang.String useKernelEstimatorTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getUseKernelEstimator

public boolean getUseKernelEstimator()

Gets if kernel estimator is being used.

Returns:

Value of m_UseKernelEstimatory.

setUseKernelEstimator

public void setUseKernelEstimator(boolean v)

Sets if kernel estimator is to be used.

Parameters:

v - Value to assign to m_UseKernelEstimatory.

useSupervisedDiscretizationTipText

public java.lang.String useSupervisedDiscretizationTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getUseSupervisedDiscretization

public boolean getUseSupervisedDiscretization()

Get whether supervised discretization is to be used.

Returns:

true if supervised discretization is to be used.

setUseSupervisedDiscretization

public void setUseSupervisedDiscretization(boolean newblah)

Set whether supervised discretization is to be used.

Parameters:

newblah - true if supervised discretization is to be used.

main

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

Main method for testing this class.

Parameters:

argv - the options