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, CapabilitiesIgnorer, 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: 11325 $
  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 interface weka.core.Drawable

      `BayesNet, Newick, NOT_DRAWABLE, TREE`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `BayesNet()`

  - ### Method Summary

    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 weka.classifiers.AbstractClassifier

      `classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClassifier, setDebug, setDoNotCheckCapabilities`

    - ### 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

    - #### 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