weka.classifiers.bayes.net.search.local

Class SimulatedAnnealing

java.lang.Object

weka.classifiers.bayes.net.search.SearchAlgorithm

weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm

weka.classifiers.bayes.net.search.local.SimulatedAnnealing

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class SimulatedAnnealing
extends LocalScoreSearchAlgorithm
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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
 

Version:

$Revision: 11267 $

Author:

Remco Bouckaert (rrb@xm.co.nz)

See Also:

Serialized Form

Field Summary

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

TAGS_SCORE_TYPE

Constructor Summary

Constructors

Constructor and Description
SimulatedAnnealing()

Method Summary

Modifier and Type	Method and Description
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<Option>	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.local.LocalScoreSearchAlgorithm

buildStructure, 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

Methods inherited from interface weka.core.OptionHandler

makeCopy

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 network

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<Option> 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:

 -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 [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 search algorithm.

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.

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 LocalScoreSearchAlgorithm

Returns:

the revision