java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.bayes.BayesNet
  - - weka.classifiers.bayes.net.EditableBayesNet
    - - weka.classifiers.bayes.net.BayesNetGenerator

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, 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.
 
```
Version:

$Revision: 1.14 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.bayes.BayesNet
  m_Distributions, m_Instances
- Fields inherited from interface weka.core.Drawable
  BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor Description

BayesNetGenerator()
Constructor for BayesNetGenerator.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	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`	`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, 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.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

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 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

Class BayesNetGenerator

Field Summary

Fields inherited from class weka.classifiers.bayes.BayesNet

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

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

Methods inherited from class weka.classifiers.bayes.BayesNet

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

BayesNetGenerator

Method Detail

generateRandomNetwork

Init

generateRandomNetworkStructure

generateInstances

toString

listOptions

setOptions

getOptions

getRevision

main