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## weka.classifiers.bayes.net.search.global
Class SimulatedAnnealing

    java.lang.Object
      weka.classifiers.bayes.net.search.SearchAlgorithm
          weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
              weka.classifiers.bayes.net.search.global.SimulatedAnnealing

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

    public class SimulatedAnnealing

extends GlobalScoreSearchAlgorithm
implements TechnicalInformationHandler

This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.
For more information see:
R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to Inference. Utrecht, Netherlands.

BibTeX:

     @phdthesis{Bouckaert1995,
        address = {Utrecht, Netherlands},
        author = {R.R. Bouckaert},
        institution = {University of Utrecht},
        title = {Bayesian Belief Networks: from Construction to Inference},
        year = {1995}
     }

Valid options are:

     -A <float>
      Start temperature

     -U <integer>
      Number of runs

     -D <float>
      Delta temperature

     -R <seed>
      Random number seed

     -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 [LOO-CV|k-Fold-CV|Cumulative-CV]
      Score type (LOO-CV,k-Fold-CV,Cumulative-CV)

     -Q
      Use probabilistic or 0/1 scoring.
      (default probabilistic scoring)

Version:
  $Revision: 1.6 $
Author:
  Remco Bouckaert (rrb@xm.co.nz)
See Also:
  Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
`TAGS_CV_TYPE`

Constructor Summary
`SimulatedAnnealing()`

Method Summary
`java.lang.String` | `deltaTipText()`
`double` | `getDelta()`
`java.lang.String[]` | `getOptions()` Gets the current settings of the search algorithm.
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getRuns()`
`int` | `getSeed()`
`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.
`double` | `getTStart()`
`java.lang.String` | `globalInfo()` This will return a string describing the classifier.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`java.lang.String` | `runsTipText()`
`void` | `search(BayesNet bayesNet, Instances instances)`
`java.lang.String` | `seedTipText()`
`void` | `setDelta(double fDelta)` Sets the m_fDelta.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setRuns(int nRuns)` Sets the m_nRuns.
`void` | `setSeed(int nSeed)` Sets the random number seed
`void` | `setTStart(double fTStart)` Sets the m_fTStart.
`java.lang.String` | `TStartTipText()`

Methods inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
`calcScore, calcScoreWithExtraParent, calcScoreWithMissingParent, calcScoreWithReversedParent, cumulativeCV, CVTypeTipText, getCVType, getMarkovBlanketClassifier, getUseProb, kFoldCV, leaveOneOutCV, markovBlanketClassifierTipText, setCVType, setMarkovBlanketClassifier, setUseProb, useProbTipText`

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

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

Constructor Detail

### SimulatedAnnealing

    public SimulatedAnnealing()

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

### search

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

  Parameters:
    `bayesNet` - the bayes net to use
    `instances` - the data to use
  Throws:
    `java.lang.Exception` - if something goes wrong

### getDelta

    public double getDelta()

  Returns:
    double

### getTStart

    public double getTStart()

  Returns:
    double

### getRuns

    public int getRuns()

  Returns:
    int

### setDelta

    public void setDelta(double fDelta)

  Sets the m_fDelta.

  Parameters:
    `fDelta` - The m_fDelta to set

### setTStart

    public void setTStart(double fTStart)

  Sets the m_fTStart.

  Parameters:
    `fTStart` - The m_fTStart to set

### setRuns

    public void setRuns(int nRuns)

  Sets the m_nRuns.

  Parameters:
    `nRuns` - The m_nRuns to set

### getSeed

    public int getSeed()

  Returns:
    random number seed

### setSeed

    public void setSeed(int nSeed)

  Sets the random number seed

  Parameters:
    `nSeed` - The number of the seed to set

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

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

       -A <float>
        Start temperature

       -U <integer>
        Number of runs

       -D <float>
        Delta temperature

       -R <seed>
        Random number seed

       -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 [LOO-CV|k-Fold-CV|Cumulative-CV]
        Score type (LOO-CV,k-Fold-CV,Cumulative-CV)

       -Q
        Use probabilistic or 0/1 scoring.
        (default probabilistic scoring)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `GlobalScoreSearchAlgorithm`
  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 search algorithm.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `GlobalScoreSearchAlgorithm`
  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 `GlobalScoreSearchAlgorithm`
  Returns:
    The string.

### TStartTipText

    public java.lang.String TStartTipText()

  Returns:
    a string to describe the TStart option.

### runsTipText

    public java.lang.String runsTipText()

  Returns:
    a string to describe the Runs option.

### deltaTipText

    public java.lang.String deltaTipText()

  Returns:
    a string to describe the Delta option.

### seedTipText

    public java.lang.String seedTipText()

  Returns:
    a string to describe the Seed option.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

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