BayesNet

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.bayes.BayesNet

All Implemented Interfaces:

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

Direct Known Subclasses:

BIFReader, EditableBayesNet
```
public class BayesNet
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer
```
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://sourceforge.net/projects/weka/files/documentation/WekaManual-3-7-0.pdf /download
Valid options are:
```
 -D
  Do not use ADTree data structure
 
```
```
 -B <BIF file>
  BIF file to compare with
 
```
```
 -Q weka.classifiers.bayes.net.search.SearchAlgorithm
  Search algorithm
 
```
```
 -E weka.classifiers.bayes.net.estimate.SimpleEstimator
  Estimator algorithm
 
```
Version:

$Revision: 15520 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`Estimator[][]`	`m_Distributions` The attribute estimators containing CPTs.
`Instances`	`m_Instances` The dataset header for the purposes of printing out a semi-intelligible model

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

BayesNet()

Constructors
Constructor and Description
`BayesNet()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`BIFFileTipText()`
`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.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the measure names.
`void`	`estimateCPTs()` estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
`java.lang.String`	`estimatorTipText()` This will return a string describing the BayesNetEstimator.
`ADNode`	`getADTree()` get ADTree strucrture containing efficient representation of counts.
`java.lang.String`	`getBIFFile()` Get name of network in BIF file to compare with
`java.lang.String`	`getBIFHeader()`
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`int`	`getCardinality(int iNode)` get number of values a node can take
`Estimator[][]`	`getDistributions()` Get full set of estimators.
`BayesNetEstimator`	`getEstimator()` Get the BayesNetEstimator used for calculating the CPTs
`double`	`getMeasure(java.lang.String measureName)` Returns the value of the named measure
`java.lang.String`	`getName()` get name of the Bayes network
`java.lang.String`	`getNodeName(int iNode)` get name of a node in the Bayes network
`java.lang.String`	`getNodeValue(int iNode, int iValue)` get name of a particular value of a node
`int`	`getNrOfNodes()` get number of nodes in the Bayes network
`int`	`getNrOfParents(int iNode)` get number of parents of a node in the network structure
`int`	`getNumInstances()` Returns the number of instances the model was built from.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`int`	`getParent(int iNode, int iParent)` get node index of a parent of a node in the network structure
`int`	`getParentCardinality(int iNode)` get number of values the collection of parents of a node can take
`ParentSet`	`getParentSet(int iNode)` get the parent set of a node
`ParentSet[]`	`getParentSets()` Get full set of parent sets.
`double`	`getProbability(int iNode, int iParent, int iValue)` get particular probability of the conditional probability distribtion of a node given its parents.
`java.lang.String`	`getRevision()` Returns the revision string.
`SearchAlgorithm`	`getSearchAlgorithm()` Get the SearchAlgorithm used as the search algorithm
`boolean`	`getUseADTree()` Method declaration
`java.lang.String`	`globalInfo()` This will return a string describing the classifier.
`java.lang.String`	`graph()` Returns a BayesNet graph in XMLBIF ver 0.3 format.
`int`	`graphType()` Returns the type of graph this classifier represents.
`void`	`initCPTs()` initializes the conditional probabilities
`void`	`initStructure()` Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureAICScore()`
`double`	`measureBayesScore()`
`double`	`measureBDeuScore()`
`double`	`measureDivergence()`
`double`	`measureEntropyScore()`
`double`	`measureExtraArcs()`
`double`	`measureMDLScore()`
`double`	`measureMissingArcs()`
`double`	`measureReversedArcs()`
`static java.lang.String[]`	`partitionOptions(java.lang.String[] options)` Returns the secondary set of options (if any) contained in the supplied options array.
`java.lang.String`	`searchAlgorithmTipText()`
`void`	`setBIFFile(java.lang.String sBIFFile)` Set name of network in BIF file to compare with
`void`	`setEstimator(BayesNetEstimator newBayesNetEstimator)` Set the Estimator Algorithm used in calculating the CPTs
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)` Set the SearchAlgorithm used in searching for network structures.
`void`	`setUseADTree(boolean bUseADTree)` Set whether ADTree structure is used or not
`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 java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - m_Distributions
```
public Estimator[][] m_Distributions
```
    The attribute estimators containing CPTs.
  - m_Instances
```
public Instances m_Instances
```
    The dataset header for the purposes of printing out a semi-intelligible model
- Constructor Detail
  - BayesNet
```
public BayesNet()
```
- Method Detail
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - getNumInstances
```
public int getNumInstances()
```
    Returns the number of instances the model was built from.
  - 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).
    
    Throws:
    
    java.lang.Exception - in case of an error
  - 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.
    
    Throws:
    
    java.lang.Exception - in case of an error
  - estimateCPTs
```
public void estimateCPTs()
                  throws java.lang.Exception
```
    estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
    
    Throws:
    
    java.lang.Exception - in case of an error
  - initCPTs
```
public void initCPTs()
              throws java.lang.Exception
```
    initializes the conditional probabilities
    
    Throws:
    
    java.lang.Exception - in case of an error
  - 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.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    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<Option> listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    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:
```
 -D
  Do not use ADTree data structure
 
```
```
 -B <BIF file>
  BIF file to compare with
 
```
```
 -Q weka.classifiers.bayes.net.search.SearchAlgorithm
  Search algorithm
 
```
```
 -E weka.classifiers.bayes.net.estimate.SimpleEstimator
  Estimator algorithm
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - partitionOptions
```
public static java.lang.String[] partitionOptions(java.lang.String[] options)
```
    Returns the secondary set of options (if any) contained in the supplied options array. The secondary set is defined to be any options after the first "--" but before the "-E". These options are removed from the original options array.
    
    Parameters:
    
    options - the input array of options
    
    Returns:
    
    the array of secondary options
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - setSearchAlgorithm
```
public void setSearchAlgorithm(SearchAlgorithm newSearchAlgorithm)
```
    Set the SearchAlgorithm used in searching for network structures.
    
    Parameters:
    
    newSearchAlgorithm - the SearchAlgorithm to use.
  - getSearchAlgorithm
```
public SearchAlgorithm getSearchAlgorithm()
```
    Get the SearchAlgorithm used as the search algorithm
    
    Returns:
    
    the SearchAlgorithm used as the search algorithm
  - setEstimator
```
public void setEstimator(BayesNetEstimator newBayesNetEstimator)
```
    Set the Estimator Algorithm used in calculating the CPTs
    
    Parameters:
    
    newBayesNetEstimator - the Estimator to use.
  - getEstimator
```
public BayesNetEstimator getEstimator()
```
    Get the BayesNetEstimator used for calculating the CPTs
    
    Returns:
    
    the BayesNetEstimator used.
  - setUseADTree
```
public void setUseADTree(boolean bUseADTree)
```
    Set whether ADTree structure is used or not
    
    Parameters:
    
    bUseADTree - true if an ADTree structure is used
  - getUseADTree
```
public boolean getUseADTree()
```
    Method declaration
    
    Returns:
    
    whether ADTree structure is used or not
  - setBIFFile
```
public void setBIFFile(java.lang.String sBIFFile)
```
    Set name of network in BIF file to compare with
    
    Parameters:
    
    sBIFFile - the name of the BIF file
  - getBIFFile
```
public java.lang.String getBIFFile()
```
    Get name of network in BIF file to compare with
    
    Returns:
    
    BIF file name
  - 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 - in case BIF generation fails
  - getBIFHeader
```
public java.lang.String getBIFHeader()
```
  - 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.
  - useADTreeTipText
```
public java.lang.String useADTreeTipText()
```
    Returns:
    
    a string to describe the UseADTreeoption.
  - searchAlgorithmTipText
```
public java.lang.String searchAlgorithmTipText()
```
    Returns:
    
    a string to describe the SearchAlgorithm.
  - estimatorTipText
```
public java.lang.String estimatorTipText()
```
    This will return a string describing the BayesNetEstimator.
    
    Returns:
    
    The string.
  - BIFFileTipText
```
public java.lang.String BIFFileTipText()
```
    Returns:
    
    a string to describe the BIFFile.
  - globalInfo
```
public java.lang.String globalInfo()
```
    This will return a string describing the classifier.
    
    Returns:
    
    The string.
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options
  - getName
```
public java.lang.String getName()
```
    get name of the Bayes network
    
    Returns:
    
    name of the Bayes net
  - getNrOfNodes
```
public int getNrOfNodes()
```
    get number of nodes in the Bayes network
    
    Returns:
    
    number of nodes
  - getNodeName
```
public java.lang.String getNodeName(int iNode)
```
    get name of a node in the Bayes network
    
    Parameters:
    
    iNode - index of the node
    
    Returns:
    
    name of the specified node
  - getCardinality
```
public int getCardinality(int iNode)
```
    get number of values a node can take
    
    Parameters:
    
    iNode - index of the node
    
    Returns:
    
    cardinality of the specified node
  - getNodeValue
```
public java.lang.String getNodeValue(int iNode,
                                     int iValue)
```
    get name of a particular value of a node
    
    Parameters:
    
    iNode - index of the node
    
    iValue - index of the value
    
    Returns:
    
    cardinality of the specified node
  - getNrOfParents
```
public int getNrOfParents(int iNode)
```
    get number of parents of a node in the network structure
    
    Parameters:
    
    iNode - index of the node
    
    Returns:
    
    number of parents of the specified node
  - getParent
```
public int getParent(int iNode,
                     int iParent)
```
    get node index of a parent of a node in the network structure
    
    Parameters:
    
    iNode - index of the node
    
    iParent - index of the parents, e.g., 0 is the first parent, 1 the second parent, etc.
    
    Returns:
    
    node index of the iParent's parent of the specified node
  - getParentSets
```
public ParentSet[] getParentSets()
```
    Get full set of parent sets.
    
    Returns:
    
    parent sets;
  - getDistributions
```
public Estimator[][] getDistributions()
```
    Get full set of estimators.
    
    Returns:
    
    estimators;
  - getParentCardinality
```
public int getParentCardinality(int iNode)
```
    get number of values the collection of parents of a node can take
    
    Parameters:
    
    iNode - index of the node
    
    Returns:
    
    cardinality of the parent set of the specified node
  - getProbability
```
public double getProbability(int iNode,
                             int iParent,
                             int iValue)
```
    get particular probability of the conditional probability distribtion of a node given its parents.
    
    Parameters:
    
    iNode - index of the node
    
    iParent - index of the parent set, 0 <= iParent <= getParentCardinality(iNode)
    
    iValue - index of the value, 0 <= iValue <= getCardinality(iNode)
    
    Returns:
    
    probability
  - getParentSet
```
public ParentSet getParentSet(int iNode)
```
    get the parent set of a node
    
    Parameters:
    
    iNode - index of the node
    
    Returns:
    
    Parent set of the specified node.
  - getADTree
```
public ADNode getADTree()
```
    get ADTree strucrture containing efficient representation of counts.
    
    Returns:
    
    ADTree strucrture
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the measure names. Additional measures must follow the naming convention of starting with "measure", eg. double measureBlah()
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - measureExtraArcs
```
public double measureExtraArcs()
```
  - measureMissingArcs
```
public double measureMissingArcs()
```
  - measureReversedArcs
```
public double measureReversedArcs()
```
  - measureDivergence
```
public double measureDivergence()
```
  - measureBayesScore
```
public double measureBayesScore()
```
  - measureBDeuScore
```
public double measureBDeuScore()
```
  - measureMDLScore
```
public double measureMDLScore()
```
  - measureAICScore
```
public double measureAICScore()
```
  - measureEntropyScore
```
public double measureEntropyScore()
```
  - getMeasure
```
public double getMeasure(java.lang.String measureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    measureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision

Class BayesNet

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

m_Distributions

m_Instances

Constructor Detail

BayesNet

Method Detail

getCapabilities

buildClassifier

getNumInstances

initStructure

buildStructure

estimateCPTs

initCPTs

updateClassifier

distributionForInstance

countsForInstance

listOptions

setOptions

partitionOptions

getOptions

setSearchAlgorithm

getSearchAlgorithm

setEstimator

getEstimator

setUseADTree

getUseADTree

setBIFFile

getBIFFile

toString

graphType

graph

getBIFHeader

toXMLBIF03

useADTreeTipText

searchAlgorithmTipText

estimatorTipText

BIFFileTipText

globalInfo

main

getName

getNrOfNodes

getNodeName

getCardinality

getNodeValue

getNrOfParents

getParent

getParentSets

getDistributions

getParentCardinality

getProbability

getParentSet

getADTree

enumerateMeasures

measureExtraArcs

measureMissingArcs

measureReversedArcs

measureDivergence

measureBayesScore

measureBDeuScore

measureMDLScore

measureAICScore

measureEntropyScore

getMeasure

getRevision