org.dllearner.core.configurators
Class FuzzyCELOEConfigurator

java.lang.Object
  org.dllearner.core.configurators.RefinementOperatorConfigurator
      org.dllearner.core.configurators.FuzzyCELOEConfigurator

All Implemented Interfaces:

Configurator

public class FuzzyCELOEConfigurator
extends RefinementOperatorConfigurator
implements Configurator

automatically generated, do not edit manually. run org.dllearner.scripts.ConfigJavaGenerator to update

Constructor Summary

FuzzyCELOEConfigurator(FuzzyCELOE fuzzyCELOE)

Method Summary

int	getCardinalityLimit() cardinalityLimit Gives the maximum number used in cardinality restrictions..
double	getExpansionPenaltyFactor() expansionPenaltyFactor heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones).
boolean	getFilterDescriptionsFollowingFromKB() filterDescriptionsFollowingFromKB If true, then the results will not contain suggestions, which already follow logically from the knowledge base.
static FuzzyCELOE	getFuzzyCELOE(AbstractLearningProblem learningProblem, AbstractReasonerComponent reasoningService)
boolean	getInstanceBasedDisjoints() instanceBasedDisjoints Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator..
int	getMaxClassDescriptionTests() maxClassDescriptionTests The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit).
int	getMaxDepth() maxDepth maximum depth of description.
int	getMaxExecutionTimeInSeconds() maxExecutionTimeInSeconds algorithm will stop after specified seconds.
int	getMaxNrOfResults() maxNrOfResults Sets the maximum number of results one is interested in.
double	getNoisePercentage() noisePercentage the (approximated) percentage of noise within the examples.
boolean	getReplaceSearchTree() replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree.
boolean	getReuseExistingDescription() reuseExistingDescription If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base..
String	getSearchTreeFile() searchTreeFile file to use for the search tree.
boolean	getSingleSuggestionMode() singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples..
boolean	getTerminateOnNoiseReached() terminateOnNoiseReached specifies whether to terminate when noise criterion is met.
boolean	getUseAllConstructor() useAllConstructor specifies whether the universal concept constructor is used in the learning algorithm.
boolean	getUseBooleanDatatypes() useBooleanDatatypes specifies whether boolean datatypes are used in the learning algorothm.
boolean	getUseCardinalityRestrictions() useCardinalityRestrictions specifies whether CardinalityRestrictions is used in the learning algorithm.
boolean	getUseDataHasValueConstructor() useDataHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties.
boolean	getUseDoubleDatatypes() useDoubleDatatypes specifies whether double datatypes are used in the learning algorothm.
boolean	getUseExistsConstructor() useExistsConstructor specifies whether the existential concept constructor is used in the learning algorithm.
boolean	getUseHasValueConstructor() useHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm.
boolean	getUseNegation() useNegation specifies whether negation is used in the learning algorothm.
int	getValueFrequencyThreshold() valueFrequencyThreshold specifies how often an object must occur as value in order to be considered for hasValue restrictions.
boolean	getWriteSearchTree() writeSearchTree specifies whether to write a search tree.
boolean	isReinitNecessary() true, if this component needs reinitializsation.
void	setCardinalityLimit(int cardinalityLimit)
void	setExpansionPenaltyFactor(double expansionPenaltyFactor)
void	setFilterDescriptionsFollowingFromKB(boolean filterDescriptionsFollowingFromKB)
void	setInstanceBasedDisjoints(boolean instanceBasedDisjoints)
void	setMaxClassDescriptionTests(int maxClassDescriptionTests)
void	setMaxDepth(int maxDepth)
void	setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)
void	setMaxNrOfResults(int maxNrOfResults)
void	setNoisePercentage(double noisePercentage)
void	setReplaceSearchTree(boolean replaceSearchTree)
void	setReuseExistingDescription(boolean reuseExistingDescription)
void	setSearchTreeFile(String searchTreeFile)
void	setSingleSuggestionMode(boolean singleSuggestionMode)
void	setTerminateOnNoiseReached(boolean terminateOnNoiseReached)
void	setUseAllConstructor(boolean useAllConstructor)
void	setUseBooleanDatatypes(boolean useBooleanDatatypes)
void	setUseCardinalityRestrictions(boolean useCardinalityRestrictions)
void	setUseDataHasValueConstructor(boolean useDataHasValueConstructor)
void	setUseDoubleDatatypes(boolean useDoubleDatatypes)
void	setUseExistsConstructor(boolean useExistsConstructor)
void	setUseHasValueConstructor(boolean useHasValueConstructor)
void	setUseNegation(boolean useNegation)
void	setValueFrequencyThreshold(int valueFrequencyThreshold)
void	setWriteSearchTree(boolean writeSearchTree)

Methods inherited from class org.dllearner.core.configurators.RefinementOperatorConfigurator

getUseStringDatatypes

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FuzzyCELOEConfigurator

public FuzzyCELOEConfigurator(FuzzyCELOE fuzzyCELOE)

Parameters:

fuzzyCELOE - see FuzzyCELOE

Method Detail

getFuzzyCELOE

public static FuzzyCELOE getFuzzyCELOE(AbstractLearningProblem learningProblem,
                                       AbstractReasonerComponent reasoningService)
                                throws LearningProblemUnsupportedException

Parameters:

reasoningService - see reasoningService

learningProblem - see learningProblem

Returns:

FuzzyCELOE

Throws:

LearningProblemUnsupportedException - see

getUseAllConstructor

public boolean getUseAllConstructor()

useAllConstructor specifies whether the universal concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:

getUseAllConstructor in class RefinementOperatorConfigurator

Returns:

boolean

getUseExistsConstructor

public boolean getUseExistsConstructor()

useExistsConstructor specifies whether the existential concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:

getUseExistsConstructor in class RefinementOperatorConfigurator

Returns:

boolean

getUseHasValueConstructor

public boolean getUseHasValueConstructor()

useHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: false

Overrides:

getUseHasValueConstructor in class RefinementOperatorConfigurator

Returns:

boolean

getUseDataHasValueConstructor

public boolean getUseDataHasValueConstructor()

useDataHasValueConstructor specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties. mandatory: false| reinit necessary: true default value: false

Overrides:

getUseDataHasValueConstructor in class RefinementOperatorConfigurator

Returns:

boolean

getValueFrequencyThreshold

public int getValueFrequencyThreshold()

valueFrequencyThreshold specifies how often an object must occur as value in order to be considered for hasValue restrictions. mandatory: false| reinit necessary: true default value: 3

Overrides:

getValueFrequencyThreshold in class RefinementOperatorConfigurator

Returns:

int

getUseCardinalityRestrictions

public boolean getUseCardinalityRestrictions()

useCardinalityRestrictions specifies whether CardinalityRestrictions is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

Specified by:

getUseCardinalityRestrictions in class RefinementOperatorConfigurator

Returns:

boolean

getCardinalityLimit

public int getCardinalityLimit()

cardinalityLimit Gives the maximum number used in cardinality restrictions.. mandatory: false| reinit necessary: true default value: 5

Overrides:

getCardinalityLimit in class RefinementOperatorConfigurator

Returns:

int

getUseNegation

public boolean getUseNegation()

useNegation specifies whether negation is used in the learning algorothm. mandatory: false| reinit necessary: true default value: false

Specified by:

getUseNegation in class RefinementOperatorConfigurator

Returns:

boolean

getUseBooleanDatatypes

public boolean getUseBooleanDatatypes()

useBooleanDatatypes specifies whether boolean datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

Specified by:

getUseBooleanDatatypes in class RefinementOperatorConfigurator

Returns:

boolean

getUseDoubleDatatypes

public boolean getUseDoubleDatatypes()

useDoubleDatatypes specifies whether double datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

Overrides:

getUseDoubleDatatypes in class RefinementOperatorConfigurator

Returns:

boolean

getMaxExecutionTimeInSeconds

public int getMaxExecutionTimeInSeconds()

maxExecutionTimeInSeconds algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

Returns:

int

getNoisePercentage

public double getNoisePercentage()

noisePercentage the (approximated) percentage of noise within the examples. mandatory: false| reinit necessary: true default value: 0.0

Returns:

double

getTerminateOnNoiseReached

public boolean getTerminateOnNoiseReached()

terminateOnNoiseReached specifies whether to terminate when noise criterion is met. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getMaxDepth

public int getMaxDepth()

maxDepth maximum depth of description. mandatory: false| reinit necessary: true default value: 7

Returns:

int

getMaxNrOfResults

public int getMaxNrOfResults()

maxNrOfResults Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions).. mandatory: false| reinit necessary: true default value: 10

Returns:

int

getMaxClassDescriptionTests

public int getMaxClassDescriptionTests()

maxClassDescriptionTests The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.). mandatory: false| reinit necessary: true default value: 0

Returns:

int

getSingleSuggestionMode

public boolean getSingleSuggestionMode()

singleSuggestionMode Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getInstanceBasedDisjoints

public boolean getInstanceBasedDisjoints()

instanceBasedDisjoints Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator.. mandatory: false| reinit necessary: true default value: true

Specified by:

getInstanceBasedDisjoints in class RefinementOperatorConfigurator

Returns:

boolean

getFilterDescriptionsFollowingFromKB

public boolean getFilterDescriptionsFollowingFromKB()

filterDescriptionsFollowingFromKB If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions.. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getReuseExistingDescription

public boolean getReuseExistingDescription()

reuseExistingDescription If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base.. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getWriteSearchTree

public boolean getWriteSearchTree()

writeSearchTree specifies whether to write a search tree. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getSearchTreeFile

public String getSearchTreeFile()

searchTreeFile file to use for the search tree. mandatory: false| reinit necessary: true default value: log/searchTree.txt

Returns:

String

getReplaceSearchTree

public boolean getReplaceSearchTree()

replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getExpansionPenaltyFactor

public double getExpansionPenaltyFactor()

expansionPenaltyFactor heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones). mandatory: false| reinit necessary: true default value: 0.1

Returns:

double

setUseAllConstructor

public void setUseAllConstructor(boolean useAllConstructor)

Parameters:

useAllConstructor - specifies whether the universal concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setUseExistsConstructor

public void setUseExistsConstructor(boolean useExistsConstructor)

Parameters:

useExistsConstructor - specifies whether the existential concept constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setUseHasValueConstructor

public void setUseHasValueConstructor(boolean useHasValueConstructor)

Parameters:

useHasValueConstructor - specifies whether the hasValue constructor is used in the learning algorithm. mandatory: false| reinit necessary: true default value: false

setUseDataHasValueConstructor

public void setUseDataHasValueConstructor(boolean useDataHasValueConstructor)

Parameters:

useDataHasValueConstructor - specifies whether the hasValue constructor is used in the learning algorithm in combination with data properties. mandatory: false| reinit necessary: true default value: false

setValueFrequencyThreshold

public void setValueFrequencyThreshold(int valueFrequencyThreshold)

Parameters:

valueFrequencyThreshold - specifies how often an object must occur as value in order to be considered for hasValue restrictions. mandatory: false| reinit necessary: true default value: 3

setUseCardinalityRestrictions

public void setUseCardinalityRestrictions(boolean useCardinalityRestrictions)

Parameters:

useCardinalityRestrictions - specifies whether CardinalityRestrictions is used in the learning algorithm. mandatory: false| reinit necessary: true default value: true

setCardinalityLimit

public void setCardinalityLimit(int cardinalityLimit)

Parameters:

cardinalityLimit - Gives the maximum number used in cardinality restrictions.. mandatory: false| reinit necessary: true default value: 5

setUseNegation

public void setUseNegation(boolean useNegation)

Parameters:

useNegation - specifies whether negation is used in the learning algorothm. mandatory: false| reinit necessary: true default value: false

setUseBooleanDatatypes

public void setUseBooleanDatatypes(boolean useBooleanDatatypes)

Parameters:

useBooleanDatatypes - specifies whether boolean datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

setUseDoubleDatatypes

public void setUseDoubleDatatypes(boolean useDoubleDatatypes)

Parameters:

useDoubleDatatypes - specifies whether double datatypes are used in the learning algorothm. mandatory: false| reinit necessary: true default value: true

setMaxExecutionTimeInSeconds

public void setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)

Parameters:

maxExecutionTimeInSeconds - algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

setNoisePercentage

public void setNoisePercentage(double noisePercentage)

Parameters:

noisePercentage - the (approximated) percentage of noise within the examples. mandatory: false| reinit necessary: true default value: 0.0

setTerminateOnNoiseReached

public void setTerminateOnNoiseReached(boolean terminateOnNoiseReached)

Parameters:

terminateOnNoiseReached - specifies whether to terminate when noise criterion is met. mandatory: false| reinit necessary: true default value: false

setMaxDepth

public void setMaxDepth(int maxDepth)

Parameters:

maxDepth - maximum depth of description. mandatory: false| reinit necessary: true default value: 7

setMaxNrOfResults

public void setMaxNrOfResults(int maxNrOfResults)

Parameters:

maxNrOfResults - Sets the maximum number of results one is interested in. (Setting this to a lower value may increase performance as the learning algorithm has to store/evaluate/beautify less descriptions).. mandatory: false| reinit necessary: true default value: 10

setMaxClassDescriptionTests

public void setMaxClassDescriptionTests(int maxClassDescriptionTests)

Parameters:

maxClassDescriptionTests - The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit). The algorithm will stop afterwards. (The real number of tests can be slightly higher, because this criterion usually won't be checked after each single test.). mandatory: false| reinit necessary: true default value: 0

setSingleSuggestionMode

public void setSingleSuggestionMode(boolean singleSuggestionMode)

Parameters:

singleSuggestionMode - Use this if you are interested in only one suggestion and your learning problem has many (more than 1000) examples.. mandatory: false| reinit necessary: true default value: false

setInstanceBasedDisjoints

public void setInstanceBasedDisjoints(boolean instanceBasedDisjoints)

Parameters:

instanceBasedDisjoints - Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator.. mandatory: false| reinit necessary: true default value: true

setFilterDescriptionsFollowingFromKB

public void setFilterDescriptionsFollowingFromKB(boolean filterDescriptionsFollowingFromKB)

Parameters:

filterDescriptionsFollowingFromKB - If true, then the results will not contain suggestions, which already follow logically from the knowledge base. Be careful, since this requires a potentially expensive consistency check for candidate solutions.. mandatory: false| reinit necessary: true default value: false

setReuseExistingDescription

public void setReuseExistingDescription(boolean reuseExistingDescription)

Parameters:

reuseExistingDescription - If true, the algorithm tries to find a good starting point close to an existing definition/super class of the given class in the knowledge base.. mandatory: false| reinit necessary: true default value: false

setWriteSearchTree

public void setWriteSearchTree(boolean writeSearchTree)

Parameters:

writeSearchTree - specifies whether to write a search tree. mandatory: false| reinit necessary: true default value: false

setSearchTreeFile

public void setSearchTreeFile(String searchTreeFile)

Parameters:

searchTreeFile - file to use for the search tree. mandatory: false| reinit necessary: true default value: log/searchTree.txt

setReplaceSearchTree

public void setReplaceSearchTree(boolean replaceSearchTree)

Parameters:

replaceSearchTree - specifies whether to replace the search tree in the log file after each run or append the new search tree. mandatory: false| reinit necessary: true default value: false

setExpansionPenaltyFactor

public void setExpansionPenaltyFactor(double expansionPenaltyFactor)

Parameters:

expansionPenaltyFactor - heuristic penalty per syntactic construct used (lower = finds more complex expression, but might miss simple ones). mandatory: false| reinit necessary: true default value: 0.1

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:

boolean