weka.classifiers.bayes.net

Class BayesNetGenerator

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.BayesNet

weka.classifiers.bayes.net.EditableBayesNet

weka.classifiers.bayes.net.BayesNetGenerator

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class BayesNetGenerator
extends EditableBayesNet

Bayes Network learning using various search algorithms and quality measures.
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.

For more information see:

http://www.cs.waikato.ac.nz/~remco/weka.pdf

Valid options are:

 -B
  Generate network (instead of instances)
 

 -N <integer>
  Nr of nodes
 

 -A <integer>
  Nr of arcs
 

 -M <integer>
  Nr of instances
 

 -C <integer>
  Cardinality of the variables
 

 -S <integer>
  Seed for random number generator
 

 -F <file>
  The BIF file to obtain the structure from.
 

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.BayesNet

m_Distributions, m_Instances

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor and Description
BayesNetGenerator() Constructor for BayesNetGenerator.

Method Summary

Modifier and Type	Method and Description
void	generateInstances() GenerateInstances generates random instances sampling from the distribution represented by the Bayes network structure.
void	generateRandomNetwork() Generate random connected Bayesian network with discrete nodes having all the same cardinality.
void	generateRandomNetworkStructure(int nNodes, int nArcs) GenerateRandomNetworkStructure generate random connected Bayesian network
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
void	Init(int nNodes, int nValues) Init defines a minimal Bayes net with no arcs
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] args) Main method
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns either the net (if BIF format) or the generated instances

Methods inherited from class weka.classifiers.bayes.net.EditableBayesNet

addArc, addArc, addArc, addNode, addNode, addNodeValue, alignBottom, alignLeft, alignRight, alignTop, canRedo, canUndo, centerHorizontal, centerVertical, clearUndoStack, deleteArc, deleteArc, deleteNode, deleteNode, deleteSelection, delNodeValue, getChildren, getContent, getDistribution, getDistribution, getEvidence, getMargin, getNode, getNode2, getPositionX, getPositionY, getValueName, getValues, getValues, isChanged, isSaved, lastActionMsg, layoutGraph, paste, redo, renameNodeValue, setData, setDistribution, setDistribution, setEvidence, setMargin, setNodeName, setPosition, setPosition, spaceHorizontal, spaceVertical, toXMLBIF03, toXMLBIF03, undo

Methods inherited from class weka.classifiers.bayes.BayesNet

BIFFileTipText, buildClassifier, buildStructure, countsForInstance, distributionForInstance, enumerateMeasures, estimateCPTs, estimatorTipText, getADTree, getBIFFile, getBIFHeader, getCapabilities, getCardinality, getDistributions, getEstimator, getMeasure, getName, getNodeName, getNodeValue, getNrOfNodes, getNrOfParents, getNumInstances, getParent, getParentCardinality, getParentSet, getParentSets, getProbability, getSearchAlgorithm, getUseADTree, globalInfo, graph, graphType, initCPTs, initStructure, measureAICScore, measureBayesScore, measureBDeuScore, measureDivergence, measureEntropyScore, measureExtraArcs, measureMDLScore, measureMissingArcs, measureReversedArcs, partitionOptions, searchAlgorithmTipText, setBIFFile, setEstimator, setSearchAlgorithm, setUseADTree, updateClassifier, useADTreeTipText

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Constructor Detail

BayesNetGenerator

public BayesNetGenerator()

Constructor for BayesNetGenerator.

Method Detail

generateRandomNetwork

public void generateRandomNetwork()
                           throws java.lang.Exception

Generate random connected Bayesian network with discrete nodes having all the same cardinality.

Throws:

java.lang.Exception - if something goes wrong

Init

public void Init(int nNodes,
                 int nValues)
          throws java.lang.Exception

Init defines a minimal Bayes net with no arcs

Parameters:

nNodes - number of nodes in the Bayes net

nValues - number of values each of the nodes can take

Throws:

java.lang.Exception - if something goes wrong

generateRandomNetworkStructure

public void generateRandomNetworkStructure(int nNodes,
                                           int nArcs)
                                    throws java.lang.Exception

GenerateRandomNetworkStructure generate random connected Bayesian network

Parameters:

nNodes - number of nodes in the Bayes net to generate

nArcs - number of arcs to generate. Must be between nNodes - 1 and nNodes * (nNodes-1) / 2

Throws:

java.lang.Exception - if number of arcs is incorrect

generateInstances

public void generateInstances()
                       throws java.lang.Exception

GenerateInstances generates random instances sampling from the distribution represented by the Bayes network structure. It assumes a Bayes network structure has been initialized

Throws:

java.lang.Exception - if something goes wrong

toString

public java.lang.String toString()

Returns either the net (if BIF format) or the generated instances

Overrides:

toString in class BayesNet

Returns:

either the net or the generated instances

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class BayesNet

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:

 -B
  Generate network (instead of instances)
 

 -N <integer>
  Nr of nodes
 

 -A <integer>
  Nr of arcs
 

 -M <integer>
  Nr of instances
 

 -C <integer>
  Cardinality of the variables
 

 -S <integer>
  Seed for random number generator
 

 -F <file>
  The BIF file to obtain the structure from.
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class BayesNet

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 BayesNet

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 EditableBayesNet

Returns:

the revision

main

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

Main method

Parameters:

args - the commandline parameters