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## weka.classifiers.bayes.net.search.local
Class TAN

    java.lang.Object
      weka.classifiers.bayes.net.search.SearchAlgorithm
          weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
              weka.classifiers.bayes.net.search.local.TAN

All Implemented Interfaces:
  java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class TAN

extends LocalScoreSearchAlgorithm
implements TechnicalInformationHandler

This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.
For more information see:
N. Friedman, D. Geiger, M. Goldszmidt (1997). Bayesian network classifiers. Machine Learning. 29(2-3):131-163.

BibTeX:

     @article{Friedman1997,
        author = {N. Friedman and D. Geiger and M. Goldszmidt},
        journal = {Machine Learning},
        number = {2-3},
        pages = {131-163},
        title = {Bayesian network classifiers},
        volume = {29},
        year = {1997}
     }

Valid options are:

     -mbc
      Applies a Markov Blanket correction to the network structure,
      after a network structure is learned. This ensures that all
      nodes in the network are part of the Markov blanket of the
      classifier node.

     -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
      Score type (BAYES, BDeu, MDL, ENTROPY and AIC)

Version:
  $Revision: 5333 $
Author:
  Remco Bouckaert
See Also:
  Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
`TAGS_SCORE_TYPE`

Constructor Summary
`TAN()`

Method Summary
`void` | `buildStructure(BayesNet bayesNet, Instances instances)` buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String` | `globalInfo()` This will return a string describing the classifier.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.

Methods inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
`calcNodeScore, calcScoreWithExtraParent, calcScoreWithMissingParent, getMarkovBlanketClassifier, getScoreType, logScore, markovBlanketClassifierTipText, scoreTypeTipText, setMarkovBlanketClassifier, setScoreType`

Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm
`initAsNaiveBayesTipText, maxNrOfParentsTipText, toString`

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

Constructor Detail

### TAN

    public TAN()

Method Detail

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### buildStructure

    public void buildStructure(BayesNet bayesNet,
                               Instances instances)
                        throws java.lang.Exception

  buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu

  Overrides:
    `buildStructure` in class `LocalScoreSearchAlgorithm`
  Parameters:
    `bayesNet` - the network
    `instances` - the data to use
  Throws:
    `java.lang.Exception` - if something goes wrong

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `LocalScoreSearchAlgorithm`
  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:

       -mbc
        Applies a Markov Blanket correction to the network structure,
        after a network structure is learned. This ensures that all
        nodes in the network are part of the Markov blanket of the
        classifier node.

       -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
        Score type (BAYES, BDeu, MDL, ENTROPY and AIC)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `LocalScoreSearchAlgorithm`
  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 `LocalScoreSearchAlgorithm`
  Returns:
    an array of strings suitable for passing to setOptions

### globalInfo

    public java.lang.String globalInfo()

  This will return a string describing the classifier.

  Overrides:
    `globalInfo` in class `LocalScoreSearchAlgorithm`
  Returns:
    The string.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `LocalScoreSearchAlgorithm`
  Returns:
    the revision

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