ROLComponent2Configurator (DL-Learner Javadoc)

Overview

Package

Class

Use

Tree

Deprecated

Index

Help

PREV CLASS NEXT CLASS

FRAMES NO FRAMES

SUMMARY: NESTED | FIELD | CONSTR | METHOD

DETAIL: FIELD | CONSTR | METHOD

org.dllearner.core.configurators
Class ROLComponent2Configurator

java.lang.Object
  org.dllearner.core.configurators.ROLComponent2Configurator

All Implemented Interfaces:: Configurator

public class ROLComponent2Configurator
extends Object
implements Configurator
extends Object
implements Configurator

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

Constructor Summary
`ROLComponent2Configurator(ROLComponent2 rOLComponent2)`

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).
`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`	`getMaxPosOnlyExpansion()` maxPosOnlyExpansion specifies how often a node in the search tree of a posonly learning problem needs to be expanded before it is considered as solution candidate.
`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.
`boolean`	`getReplaceSearchTree()` replaceSearchTree specifies whether to replace the search tree in the log file after each run or append the new search tree.
`static ROLComponent2`	`getROLComponent2(LearningProblem learningProblem, ReasonerComponent reasoningService)`
`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`	`getUseDoubleDatatypes()` useDoubleDatatypes specifies whether boolean 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`	`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`	`setLogLevel(String logLevel)`
`void`	`setMaxClassDescriptionTests(int maxClassDescriptionTests)`
`void`	`setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)`
`void`	`setMaxPosOnlyExpansion(int maxPosOnlyExpansion)`
`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`	`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`	`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

ROLComponent2Configurator

public ROLComponent2Configurator(ROLComponent2 rOLComponent2)

Parameters:: rOLComponent2 - see ROLComponent2

Method Detail

getROLComponent2

public static ROLComponent2 getROLComponent2(LearningProblem learningProblem,
                                             ReasonerComponent reasoningService)
                                      throws LearningProblemUnsupportedException

Parameters:: reasoningService - see reasoningService; learningProblem - see learningProblem
Returns:: ROLComponent2
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

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

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

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

Returns:: int

getUseCardinalityRestrictions

public boolean getUseCardinalityRestrictions()

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

Returns:: boolean

getCardinalityLimit

public int getCardinalityLimit()

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

Returns:: int

getUseNegation

public boolean getUseNegation()

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

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

Returns:: boolean

getUseDoubleDatatypes

public boolean getUseDoubleDatatypes()

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

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

getMaxPosOnlyExpansion

public int getMaxPosOnlyExpansion()

maxPosOnlyExpansion specifies how often a node in the search tree of a posonly learning problem needs to be expanded before it is considered as solution candidate. mandatory: false| reinit necessary: true default value: 4

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: 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

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

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 boolean 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: 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

setMaxPosOnlyExpansion

public void setMaxPosOnlyExpansion(int maxPosOnlyExpansion)

Parameters:: maxPosOnlyExpansion - specifies how often a node in the search tree of a posonly learning problem needs to be expanded before it is considered as solution candidate. mandatory: false| reinit necessary: true default value: 4

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

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:: boolean