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java.lang.Objectweka.classifiers.bayes.net.search.SearchAlgorithm
weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
weka.classifiers.bayes.net.search.local.SimulatedAnnealing
public class SimulatedAnnealing
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.
@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)
Field Summary |
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Fields inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm |
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TAGS_SCORE_TYPE |
Constructor Summary | |
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SimulatedAnnealing()
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Method Summary | |
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java.lang.String |
deltaTipText()
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double |
getDelta()
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java.lang.String[] |
getOptions()
Gets the current settings of the search algorithm. |
java.lang.String |
getRevision()
Returns the revision string. |
int |
getRuns()
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int |
getSeed()
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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()
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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()
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void |
search(BayesNet bayesNet,
Instances instances)
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java.lang.String |
seedTipText()
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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()
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Methods inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm |
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buildStructure, calcNodeScore, calcScoreWithExtraParent, calcScoreWithMissingParent, getMarkovBlanketClassifier, getScoreType, logScore, markovBlanketClassifierTipText, scoreTypeTipText, setMarkovBlanketClassifier, setScoreType |
Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm |
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initAsNaiveBayesTipText, maxNrOfParentsTipText, toString |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public SimulatedAnnealing()
Method Detail |
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public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public void search(BayesNet bayesNet, Instances instances) throws java.lang.Exception
bayesNet
- the networkinstances
- the data to use
java.lang.Exception
- if something goes wrongpublic double getDelta()
public double getTStart()
public int getRuns()
public void setDelta(double fDelta)
fDelta
- The m_fDelta to setpublic void setTStart(double fTStart)
fTStart
- The m_fTStart to setpublic void setRuns(int nRuns)
nRuns
- The m_nRuns to setpublic int getSeed()
public void setSeed(int nSeed)
nSeed
- The number of the seed to setpublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class LocalScoreSearchAlgorithm
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
setOptions
in interface OptionHandler
setOptions
in class LocalScoreSearchAlgorithm
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class LocalScoreSearchAlgorithm
public java.lang.String globalInfo()
globalInfo
in class LocalScoreSearchAlgorithm
public java.lang.String TStartTipText()
public java.lang.String runsTipText()
public java.lang.String deltaTipText()
public java.lang.String seedTipText()
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class LocalScoreSearchAlgorithm
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