weka.classifiers.bayes
Class BayesNet

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.bayes.BayesNet

All Implemented Interfaces:

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

Direct Known Subclasses:

BayesNetB, BayesNetK2

public class BayesNet

extends Classifier

implements OptionHandler, WeightedInstancesHandler, Drawable

Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B. Works with nominal variables and no missing values only.

Version:

$Revision: 1.10 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary

Instances	m_Instances The dataset header for the purposes of printing out a semi-intelligible model
static Tag[]	TAGS_SCORE_TYPE

Fields inherited from interface weka.core.Drawable

BayesNet, NOT_DRAWABLE, TREE

Constructor Summary

BayesNet()

Method Summary

java.lang.String	alphaTipText()
void	buildClassifier(Instances instances) Generates the classifier.
void	buildStructure() buildStructure determines the network structure/graph of the network.
double[]	countsForInstance(Instance instance) Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
void	estimateCPTs() estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
double	getAlpha() Method declaration
boolean	getInitAsNaiveBayes() Method declaration
int	getMaxNrOfParents() Method declaration
java.lang.String[]	getOptions() Gets the current settings of the classifier.
SelectedTag	getScoreType() Method declaration
boolean	getUseADTree() Method declaration
java.lang.String	graph() Returns a BayesNet graph in XMLBIF ver 0.3 format.
int	graphType() Returns the type of graph this classifier represents.
java.lang.String	initAsNaiveBayesTipText()
void	initStructure() Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
double	logScore(int nType) logScore returns the log of the quality of a network (e.g.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	maxNrOfParentsTipText()
java.lang.String	scoreTypeTipText()
void	setAlpha(double fAlpha) Method declaration
void	setInitAsNaiveBayes(boolean bInitAsNaiveBayes) Method declaration
void	setMaxNrOfParents(int nMaxNrOfParents) Method declaration
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setScoreType(SelectedTag newScoreType) Method declaration
void	setUseADTree(boolean bUseADTree) Method declaration
java.lang.String	toString() Returns a description of the classifier.
java.lang.String	toXMLBIF03() Returns a description of the classifier in XML BIF 0.3 format.
void	updateClassifier(Instance instance) Updates the classifier with the given instance.
java.lang.String	useADTreeTipText()

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_Instances

public Instances m_Instances

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

TAGS_SCORE_TYPE

public static final Tag[] TAGS_SCORE_TYPE

Constructor Detail

BayesNet

public BayesNet()

Method Detail

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

initStructure

public void initStructure()
                   throws java.lang.Exception

Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).

java.lang.Exception

buildStructure

public void buildStructure()
                    throws java.lang.Exception

buildStructure determines the network structure/graph of the network. The default behavior is creating a network where all nodes have the first node as its parent (i.e., a BayesNet that behaves like a naive Bayes classifier). This method can be overridden by derived classes to restrict the class of network structures that are acceptable.

java.lang.Exception

estimateCPTs

public void estimateCPTs()
                  throws java.lang.Exception

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

java.lang.Exception

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

countsForInstance

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

Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

counts for Dirichlet distribution for class probability

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:

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

setScoreType

public void setScoreType(SelectedTag newScoreType)

Method declaration

getScoreType

public SelectedTag getScoreType()

Method declaration

Returns:

setAlpha

public void setAlpha(double fAlpha)

Method declaration

Parameters:

fAlpha -

getAlpha

public double getAlpha()

Method declaration

Returns:

setInitAsNaiveBayes

public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes)

Method declaration

Parameters:

bInitAsNaiveBayes -

getInitAsNaiveBayes

public boolean getInitAsNaiveBayes()

Method declaration

Returns:

setUseADTree

public void setUseADTree(boolean bUseADTree)

Method declaration

Parameters:

bUseADTree -

getUseADTree

public boolean getUseADTree()

Method declaration

Returns:

setMaxNrOfParents

public void setMaxNrOfParents(int nMaxNrOfParents)

Method declaration

Parameters:

nMaxNrOfParents -

getMaxNrOfParents

public int getMaxNrOfParents()

Method declaration

Returns:

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

logScore

public double logScore(int nType)

logScore returns the log of the quality of a network (e.g. the posterior probability of the network, or the MDL value).

Parameters:

nType - score type (Bayes, MDL, etc) to calculate score with

Returns:

log score.

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.

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

Drawable.TREE

graph

public java.lang.String graph()
                       throws java.lang.Exception

Returns a BayesNet graph in XMLBIF ver 0.3 format.

Specified by:

graph in interface Drawable

Returns:

- String representing this BayesNet in XMLBIF ver 0.3

Throws:

java.lang.Exception - if the graph can't be computed

toXMLBIF03

public java.lang.String toXMLBIF03()

Returns a description of the classifier in XML BIF 0.3 format. See http://www-2.cs.cmu.edu/~fgcozman/Research/InterchangeFormat/ for details on XML BIF.

Returns:

an XML BIF 0.3 description of the classifier as a string.

scoreTypeTipText

public java.lang.String scoreTypeTipText()

Returns:

a string to describe the ScoreType option.

alphaTipText

public java.lang.String alphaTipText()

Returns:

a string to describe the Alpha option.

initAsNaiveBayesTipText

public java.lang.String initAsNaiveBayesTipText()

Returns:

a string to describe the InitAsNaiveBayes option.

useADTreeTipText

public java.lang.String useADTreeTipText()

Returns:

a string to describe the UseADTreeoption.

maxNrOfParentsTipText

public java.lang.String maxNrOfParentsTipText()

Returns:

a string to describe the MaxNrOfParentsoption.

main

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

Main method for testing this class.

Parameters:

argv - the options