weka.classifiers.bayes.net.estimate

Class MultiNomialBMAEstimator

java.lang.Object

weka.classifiers.bayes.net.estimate.BayesNetEstimator

weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler

public class MultiNomialBMAEstimator
extends BayesNetEstimator

Multinomial BMA Estimator.

Valid options are:

 -k2
  Whether to use K2 prior.
 

 -A <alpha>
  Initial count (alpha)
 

Version:

$Revision: 12470 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultiNomialBMAEstimator()

Method Summary

Modifier and Type	Method and Description
double[]	distributionForInstance(BayesNet bayesNet, Instance instance) Calculates the class membership probabilities for the given test instance.
void	estimateCPTs(BayesNet bayesNet) estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing this object
void	initCPTs(BayesNet bayesNet) initCPTs reserves space for CPTs and set all counts to zero
boolean	isUseK2Prior()
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseK2Prior(boolean bUseK2Prior) Sets the UseK2Prior.
void	updateClassifier(BayesNet bayesNet, Instance instance) Updates the classifier with the given instance.

Methods inherited from class weka.classifiers.bayes.net.estimate.BayesNetEstimator

alphaTipText, getAlpha, setAlpha

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

MultiNomialBMAEstimator

public MultiNomialBMAEstimator()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this object

Overrides:

globalInfo in class BayesNetEstimator

Returns:

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

estimateCPTs

public void estimateCPTs(BayesNet bayesNet)
                  throws java.lang.Exception

estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.

Overrides:

estimateCPTs in class BayesNetEstimator

Parameters:

bayesNet - the bayes net to use

Throws:

java.lang.Exception - if number of parents doesn't fit (more than 1)

updateClassifier

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

Updates the classifier with the given instance.

Overrides:

updateClassifier in class BayesNetEstimator

Parameters:

bayesNet - the bayes net to use

instance - the new training instance to include in the model

Throws:

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

initCPTs

public void initCPTs(BayesNet bayesNet)
              throws java.lang.Exception

initCPTs reserves space for CPTs and set all counts to zero

Overrides:

initCPTs in class BayesNetEstimator

Parameters:

bayesNet - the bayes net to use

Throws:

java.lang.Exception - doesn't apply

isUseK2Prior

public boolean isUseK2Prior()

Returns:

boolean

setUseK2Prior

public void setUseK2Prior(boolean bUseK2Prior)

Sets the UseK2Prior.

Parameters:

bUseK2Prior - The bUseK2Prior to set

distributionForInstance

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

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class BayesNetEstimator

Parameters:

bayesNet - the bayes net to use

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<Option> listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class BayesNetEstimator

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:

 -k2
  Whether to use K2 prior.
 

 -A <alpha>
  Initial count (alpha)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class BayesNetEstimator

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 BayesNetEstimator

Returns:

an array of strings suitable for passing to setOptions

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class BayesNetEstimator

Returns:

the revision