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## weka.classifiers.bayes
Class BayesNet

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

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, 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: 5928 $
Author:
  Remco Bouckaert (rrb@xm.co.nz)
See Also:
  Serialized Form

Field Summary
`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 interface weka.core.Drawable
`BayesNet, Newick, NOT_DRAWABLE, TREE`

Constructor Summary
`BayesNet()`

Method Summary
`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` | `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
`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` | `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 weka.classifiers.AbstractClassifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

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

### 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 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 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

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