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java.lang.Objectweka.classifiers.bayes.net.search.SearchAlgorithm
weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
weka.classifiers.bayes.net.search.global.GeneticSearch
public class GeneticSearch
This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure. Genetic search works by having a population of Bayes network structures and allow them to mutate and apply cross over to get offspring. The best network structure found during the process is returned.
Valid options are:-L <integer> Population size
-A <integer> Descendant population size
-U <integer> Number of runs
-M Use mutation. (default true)
-C Use cross-over. (default true)
-O Use tournament selection (true) or maximum subpopulatin (false). (default false)
-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)
Field Summary |
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Fields inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm |
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TAGS_CV_TYPE |
Constructor Summary | |
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GeneticSearch()
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Method Summary | |
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java.lang.String |
descendantPopulationSizeTipText()
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int |
getDescendantPopulationSize()
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java.lang.String[] |
getOptions()
Gets the current settings of the search algorithm. |
int |
getPopulationSize()
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java.lang.String |
getRevision()
Returns the revision string. |
int |
getRuns()
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int |
getSeed()
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boolean |
getUseCrossOver()
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boolean |
getUseMutation()
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boolean |
getUseTournamentSelection()
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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 |
populationSizeTipText()
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java.lang.String |
runsTipText()
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java.lang.String |
seedTipText()
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void |
setDescendantPopulationSize(int iDescendantPopulationSize)
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void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setPopulationSize(int iPopulationSize)
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void |
setRuns(int nRuns)
Sets the number of runs |
void |
setSeed(int nSeed)
Sets the random number seed |
void |
setUseCrossOver(boolean bUseCrossOver)
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void |
setUseMutation(boolean bUseMutation)
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void |
setUseTournamentSelection(boolean bUseTournamentSelection)
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java.lang.String |
useCrossOverTipText()
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java.lang.String |
useMutationTipText()
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java.lang.String |
useTournamentSelectionTipText()
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Methods inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm |
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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 |
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buildStructure, 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 GeneticSearch()
Method Detail |
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public int getRuns()
public void setRuns(int nRuns)
nRuns
- The number of runs to setpublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class GlobalScoreSearchAlgorithm
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-L <integer> Population size
-A <integer> Descendant population size
-U <integer> Number of runs
-M Use mutation. (default true)
-C Use cross-over. (default true)
-O Use tournament selection (true) or maximum subpopulatin (false). (default false)
-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)
setOptions
in interface OptionHandler
setOptions
in class GlobalScoreSearchAlgorithm
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 GlobalScoreSearchAlgorithm
public boolean getUseCrossOver()
public boolean getUseMutation()
public int getDescendantPopulationSize()
public int getPopulationSize()
public void setUseCrossOver(boolean bUseCrossOver)
bUseCrossOver
- sets whether cross-over is usedpublic void setUseMutation(boolean bUseMutation)
bUseMutation
- sets whether mutation is usedpublic boolean getUseTournamentSelection()
public void setUseTournamentSelection(boolean bUseTournamentSelection)
bUseTournamentSelection
- sets whether Tournament Selection or Maximum Sub-Population should be usedpublic void setDescendantPopulationSize(int iDescendantPopulationSize)
iDescendantPopulationSize
- sets descendant population sizepublic void setPopulationSize(int iPopulationSize)
iPopulationSize
- sets population sizepublic int getSeed()
public void setSeed(int nSeed)
nSeed
- The number of the seed to setpublic java.lang.String globalInfo()
globalInfo
in class GlobalScoreSearchAlgorithm
public java.lang.String runsTipText()
public java.lang.String seedTipText()
public java.lang.String populationSizeTipText()
public java.lang.String descendantPopulationSizeTipText()
public java.lang.String useMutationTipText()
public java.lang.String useCrossOverTipText()
public java.lang.String useTournamentSelectionTipText()
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class GlobalScoreSearchAlgorithm
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