org.dllearner.core.configurators
Class OCELConfigurator

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

All Implemented Interfaces:

Configurator

public class OCELConfigurator
extends RefinementOperatorConfigurator
implements Configurator

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

Constructor Summary

OCELConfigurator(OCEL oCEL)

Method Summary

Set<String>	getAllowedConcepts() allowedConcepts concepts the algorithm is allowed to use.
Set<String>	getAllowedRoles() allowedRoles roles the algorithm is allowed to use.
boolean	getApplyAllFilter() applyAllFilter usage of equivalence ALL R.C AND ALL R.D = ALL R.
boolean	getApplyExistsFilter() applyExistsFilter usage of equivalence EXISTS R.C OR EXISTS R.D = EXISTS R.
int	getCardinalityLimit() cardinalityLimit Gives the maximum number used in cardinality restrictions..
double	getExpansionPenaltyFactor() expansionPenaltyFactor describes the reduction in heuristic score one is willing to accept for reducing the length of the concept by one.
boolean	getForceRefinementLengthIncrease() forceRefinementLengthIncrease specifies whether nodes should be expanded until only longer refinements are reached.
int	getGuaranteeXgoodDescriptions() guaranteeXgoodDescriptions algorithm will run until X good (100%) concept descritpions are found.
String	getHeuristic() heuristic specifiy the heuristic to use.
double	getHorizontalExpansionFactor() horizontalExpansionFactor horizontal expansion factor (see publication for description).
Set<String>	getIgnoredConcepts() ignoredConcepts concepts the algorithm must ignore.
Set<String>	getIgnoredRoles() ignoredRoles roles the algorithm must ignore.
boolean	getImproveSubsumptionHierarchy() improveSubsumptionHierarchy simplify subsumption hierarchy to reduce search space (see publication for description).
boolean	getInstanceBasedDisjoints() instanceBasedDisjoints Specifies whether to use real disjointness checks or instance based ones (no common instances) in the refinement operator..
String	getLogLevel() logLevel determines the logLevel for this component, can be {TRACE, DEBUG, INFO}.
int	getMaxClassDescriptionTests() maxClassDescriptionTests The maximum number of candidate hypothesis the algorithm is allowed to test (0 = no limit).
int	getMaxExecutionTimeInSeconds() maxExecutionTimeInSeconds algorithm will stop after specified seconds.
int	getMinExecutionTimeInSeconds() minExecutionTimeInSeconds algorithm will run at least specified seconds.
int	getNegationPenalty() negationPenalty Penalty on negations (TODO: better explanation)..
double	getNegativeWeight() negativeWeight Used to penalise errors on negative examples different from those of positive examples (lower = less importance for negatives)..
double	getNoisePercentage() noisePercentage the (approximated) percentage of noise within the examples.
static OCEL	getOCEL(AbstractLearningProblem learningProblem, AbstractReasonerComponent reasoningService)
boolean	getReplaceSearchTree() replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree.
String	getSearchTreeFile() searchTreeFile file to use for the search tree.
String	getStartClass() startClass the named class which should be used to start the algorithm (GUI: needs a widget for selecting a class).
double	getStartNodeBonus() startNodeBonus You can use this to give a heuristic bonus on the start node (= initially broader exploration of search space)..
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.
boolean	getUseOverlyGeneralList() useOverlyGeneralList try to find overly general concept without sending them to the reasoner.
boolean	getUsePropernessChecks() usePropernessChecks specifies whether to check for equivalence (i.e. discard equivalent refinements).
boolean	getUseShortConceptConstruction() useShortConceptConstruction shorten concept to see whether they already exist.
boolean	getUseStringDatatypes() useStringDatatypes specifies whether string datatypes are used in the learning algorothm.
boolean	getUseTooWeakList() useTooWeakList try to filter out too weak concepts without sending them to the reasoner.
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	setAllowedConcepts(Set<String> allowedConcepts)
void	setAllowedRoles(Set<String> allowedRoles)
void	setApplyAllFilter(boolean applyAllFilter)
void	setApplyExistsFilter(boolean applyExistsFilter)
void	setCardinalityLimit(int cardinalityLimit)
void	setExpansionPenaltyFactor(double expansionPenaltyFactor)
void	setForceRefinementLengthIncrease(boolean forceRefinementLengthIncrease)
void	setGuaranteeXgoodDescriptions(int guaranteeXgoodDescriptions)
void	setHeuristic(String heuristic)
void	setHorizontalExpansionFactor(double horizontalExpansionFactor)
void	setIgnoredConcepts(Set<String> ignoredConcepts)
void	setIgnoredRoles(Set<String> ignoredRoles)
void	setImproveSubsumptionHierarchy(boolean improveSubsumptionHierarchy)
void	setInstanceBasedDisjoints(boolean instanceBasedDisjoints)
void	setLogLevel(String logLevel)
void	setMaxClassDescriptionTests(int maxClassDescriptionTests)
void	setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)
void	setMinExecutionTimeInSeconds(int minExecutionTimeInSeconds)
void	setNegationPenalty(int negationPenalty)
void	setNegativeWeight(double negativeWeight)
void	setNoisePercentage(double noisePercentage)
void	setReplaceSearchTree(boolean replaceSearchTree)
void	setSearchTreeFile(String searchTreeFile)
void	setStartClass(String startClass)
void	setStartNodeBonus(double startNodeBonus)
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	setUseOverlyGeneralList(boolean useOverlyGeneralList)
void	setUsePropernessChecks(boolean usePropernessChecks)
void	setUseShortConceptConstruction(boolean useShortConceptConstruction)
void	setUseStringDatatypes(boolean useStringDatatypes)
void	setUseTooWeakList(boolean useTooWeakList)
void	setValueFrequencyThreshold(int valueFrequencyThreshold)
void	setWriteSearchTree(boolean writeSearchTree)

Methods inherited from class java.lang.Object

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

Constructor Detail

OCELConfigurator

public OCELConfigurator(OCEL oCEL)

Parameters:

oCEL - see OCEL

Method Detail

getOCEL

public static OCEL getOCEL(AbstractLearningProblem learningProblem,
                           AbstractReasonerComponent reasoningService)
                    throws LearningProblemUnsupportedException

Parameters:

reasoningService - see reasoningService

learningProblem - see learningProblem

Returns:

OCEL

Throws:

LearningProblemUnsupportedException - see

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

getHeuristic

public String getHeuristic()

heuristic specifiy the heuristic to use. mandatory: false| reinit necessary: true default value: lexicographic

Returns:

String

getApplyAllFilter

public boolean getApplyAllFilter()

applyAllFilter usage of equivalence ALL R.C AND ALL R.D = ALL R.(C AND D). mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getApplyExistsFilter

public boolean getApplyExistsFilter()

applyExistsFilter usage of equivalence EXISTS R.C OR EXISTS R.D = EXISTS R.(C OR D). mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getUseTooWeakList

public boolean getUseTooWeakList()

useTooWeakList try to filter out too weak concepts without sending them to the reasoner. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getUseOverlyGeneralList

public boolean getUseOverlyGeneralList()

useOverlyGeneralList try to find overly general concept without sending them to the reasoner. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getUseShortConceptConstruction

public boolean getUseShortConceptConstruction()

useShortConceptConstruction shorten concept to see whether they already exist. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getHorizontalExpansionFactor

public double getHorizontalExpansionFactor()

horizontalExpansionFactor horizontal expansion factor (see publication for description). mandatory: false| reinit necessary: true default value: 0.6

Returns:

double

getImproveSubsumptionHierarchy

public boolean getImproveSubsumptionHierarchy()

improveSubsumptionHierarchy simplify subsumption hierarchy to reduce search space (see publication for description). mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getAllowedConcepts

public Set<String> getAllowedConcepts()

allowedConcepts concepts the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

Returns:

Set(String)

getIgnoredConcepts

public Set<String> getIgnoredConcepts()

ignoredConcepts concepts the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

Returns:

Set(String)

getAllowedRoles

public Set<String> getAllowedRoles()

allowedRoles roles the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

Returns:

Set(String)

getIgnoredRoles

public Set<String> getIgnoredRoles()

ignoredRoles roles the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

Returns:

Set(String)

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: true

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

getUseStringDatatypes

public boolean getUseStringDatatypes()

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

Overrides:

getUseStringDatatypes in class RefinementOperatorConfigurator

Returns:

boolean

getMaxExecutionTimeInSeconds

public int getMaxExecutionTimeInSeconds()

maxExecutionTimeInSeconds algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 0

Returns:

int

getMinExecutionTimeInSeconds

public int getMinExecutionTimeInSeconds()

minExecutionTimeInSeconds algorithm will run at least specified seconds. mandatory: false| reinit necessary: true default value: 0

Returns:

int

getGuaranteeXgoodDescriptions

public int getGuaranteeXgoodDescriptions()

guaranteeXgoodDescriptions algorithm will run until X good (100%) concept descritpions are found. mandatory: false| reinit necessary: true default value: 1

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

getLogLevel

public String getLogLevel()

logLevel determines the logLevel for this component, can be {TRACE, DEBUG, INFO}. mandatory: false| reinit necessary: true default value: DEBUG

Returns:

String

getUsePropernessChecks

public boolean getUsePropernessChecks()

usePropernessChecks specifies whether to check for equivalence (i.e. discard equivalent refinements). mandatory: false| reinit necessary: true default value: false

Returns:

boolean

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: true

Returns:

boolean

getStartClass

public String getStartClass()

startClass the named class which should be used to start the algorithm (GUI: needs a widget for selecting a class). mandatory: false| reinit necessary: true default value: null

Returns:

String

getForceRefinementLengthIncrease

public boolean getForceRefinementLengthIncrease()

forceRefinementLengthIncrease specifies whether nodes should be expanded until only longer refinements are reached. mandatory: false| reinit necessary: true default value: null

Returns:

boolean

getNegativeWeight

public double getNegativeWeight()

negativeWeight Used to penalise errors on negative examples different from those of positive examples (lower = less importance for negatives).. mandatory: false| reinit necessary: true default value: 1.0

Returns:

double

getStartNodeBonus

public double getStartNodeBonus()

startNodeBonus You can use this to give a heuristic bonus on the start node (= initially broader exploration of search space).. mandatory: false| reinit necessary: true default value: 0.0

Returns:

double

getNegationPenalty

public int getNegationPenalty()

negationPenalty Penalty on negations (TODO: better explanation).. mandatory: false| reinit necessary: true default value: 0

Returns:

int

getExpansionPenaltyFactor

public double getExpansionPenaltyFactor()

expansionPenaltyFactor describes the reduction in heuristic score one is willing to accept for reducing the length of the concept by one. mandatory: false| reinit necessary: true default value: 0.02

Returns:

double

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

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

setHeuristic

public void setHeuristic(String heuristic)

Parameters:

heuristic - specifiy the heuristic to use. mandatory: false| reinit necessary: true default value: lexicographic

setApplyAllFilter

public void setApplyAllFilter(boolean applyAllFilter)

Parameters:

applyAllFilter - usage of equivalence ALL R.C AND ALL R.D = ALL R.(C AND D). mandatory: false| reinit necessary: true default value: true

setApplyExistsFilter

public void setApplyExistsFilter(boolean applyExistsFilter)

Parameters:

applyExistsFilter - usage of equivalence EXISTS R.C OR EXISTS R.D = EXISTS R.(C OR D). mandatory: false| reinit necessary: true default value: true

setUseTooWeakList

public void setUseTooWeakList(boolean useTooWeakList)

Parameters:

useTooWeakList - try to filter out too weak concepts without sending them to the reasoner. mandatory: false| reinit necessary: true default value: true

setUseOverlyGeneralList

public void setUseOverlyGeneralList(boolean useOverlyGeneralList)

Parameters:

useOverlyGeneralList - try to find overly general concept without sending them to the reasoner. mandatory: false| reinit necessary: true default value: true

setUseShortConceptConstruction

public void setUseShortConceptConstruction(boolean useShortConceptConstruction)

Parameters:

useShortConceptConstruction - shorten concept to see whether they already exist. mandatory: false| reinit necessary: true default value: true

setHorizontalExpansionFactor

public void setHorizontalExpansionFactor(double horizontalExpansionFactor)

Parameters:

horizontalExpansionFactor - horizontal expansion factor (see publication for description). mandatory: false| reinit necessary: true default value: 0.6

setImproveSubsumptionHierarchy

public void setImproveSubsumptionHierarchy(boolean improveSubsumptionHierarchy)

Parameters:

improveSubsumptionHierarchy - simplify subsumption hierarchy to reduce search space (see publication for description). mandatory: false| reinit necessary: true default value: true

setAllowedConcepts

public void setAllowedConcepts(Set<String> allowedConcepts)

Parameters:

allowedConcepts - concepts the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

setIgnoredConcepts

public void setIgnoredConcepts(Set<String> ignoredConcepts)

Parameters:

ignoredConcepts - concepts the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

setAllowedRoles

public void setAllowedRoles(Set<String> allowedRoles)

Parameters:

allowedRoles - roles the algorithm is allowed to use. mandatory: false| reinit necessary: true default value: null

setIgnoredRoles

public void setIgnoredRoles(Set<String> ignoredRoles)

Parameters:

ignoredRoles - roles the algorithm must ignore. mandatory: false| reinit necessary: true default value: null

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: true

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

setUseStringDatatypes

public void setUseStringDatatypes(boolean useStringDatatypes)

Parameters:

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

setMaxExecutionTimeInSeconds

public void setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)

Parameters:

maxExecutionTimeInSeconds - algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 0

setMinExecutionTimeInSeconds

public void setMinExecutionTimeInSeconds(int minExecutionTimeInSeconds)

Parameters:

minExecutionTimeInSeconds - algorithm will run at least specified seconds. mandatory: false| reinit necessary: true default value: 0

setGuaranteeXgoodDescriptions

public void setGuaranteeXgoodDescriptions(int guaranteeXgoodDescriptions)

Parameters:

guaranteeXgoodDescriptions - algorithm will run until X good (100%) concept descritpions are found. mandatory: false| reinit necessary: true default value: 1

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

setLogLevel

public void setLogLevel(String logLevel)

Parameters:

logLevel - determines the logLevel for this component, can be {TRACE, DEBUG, INFO}. mandatory: false| reinit necessary: true default value: DEBUG

setUsePropernessChecks

public void setUsePropernessChecks(boolean usePropernessChecks)

Parameters:

usePropernessChecks - specifies whether to check for equivalence (i.e. discard equivalent refinements). mandatory: false| reinit necessary: true default value: false

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: true

setStartClass

public void setStartClass(String startClass)

Parameters:

startClass - the named class which should be used to start the algorithm (GUI: needs a widget for selecting a class). mandatory: false| reinit necessary: true default value: null

setForceRefinementLengthIncrease

public void setForceRefinementLengthIncrease(boolean forceRefinementLengthIncrease)

Parameters:

forceRefinementLengthIncrease - specifies whether nodes should be expanded until only longer refinements are reached. mandatory: false| reinit necessary: true default value: null

setNegativeWeight

public void setNegativeWeight(double negativeWeight)

Parameters:

negativeWeight - Used to penalise errors on negative examples different from those of positive examples (lower = less importance for negatives).. mandatory: false| reinit necessary: true default value: 1.0

setStartNodeBonus

public void setStartNodeBonus(double startNodeBonus)

Parameters:

startNodeBonus - You can use this to give a heuristic bonus on the start node (= initially broader exploration of search space).. mandatory: false| reinit necessary: true default value: 0.0

setNegationPenalty

public void setNegationPenalty(int negationPenalty)

Parameters:

negationPenalty - Penalty on negations (TODO: better explanation).. mandatory: false| reinit necessary: true default value: 0

setExpansionPenaltyFactor

public void setExpansionPenaltyFactor(double expansionPenaltyFactor)

Parameters:

expansionPenaltyFactor - describes the reduction in heuristic score one is willing to accept for reducing the length of the concept by one. mandatory: false| reinit necessary: true default value: 0.02

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

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:

boolean