Class ClassicLStarMoore<I,O>
- java.lang.Object
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- de.learnlib.algorithm.lstar.AbstractLStar<A,I,D>
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- de.learnlib.algorithm.lstar.AbstractAutomatonLStar<A,I,D,S,T,SP,TP,AI>
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- de.learnlib.algorithm.lstar.AbstractExtensibleAutomatonLStar<MooreMachine<?,I,?,O>,I,O,Integer,Integer,O,Void,CompactMoore<I,O>>
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- de.learnlib.algorithm.lstar.moore.ClassicLStarMoore<I,O>
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- Type Parameters:
I- input symbol typeO- output symbol type
- All Implemented Interfaces:
GlobalSuffixLearner<MooreMachine<?,I,?,O>,I,O>,LearningAlgorithm<MooreMachine<?,I,?,O>,I,O>,ObservationTableFeature<I,O>,OTLearner<MooreMachine<?,I,?,O>,I,O>,Resumable<AutomatonLStarState<I,O,CompactMoore<I,O>,Integer>>,SupportsGrowingAlphabet<I>
public class ClassicLStarMoore<I,O> extends AbstractExtensibleAutomatonLStar<MooreMachine<?,I,?,O>,I,O,Integer,Integer,O,Void,CompactMoore<I,O>>
AMooreMachine-based specialization of the classic L* learner.
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Nested Class Summary
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Nested classes/interfaces inherited from class de.learnlib.algorithm.lstar.AbstractExtensibleAutomatonLStar
AbstractExtensibleAutomatonLStar.BuilderDefaults
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Nested classes/interfaces inherited from interface de.learnlib.algorithm.GlobalSuffixLearner
GlobalSuffixLearner.GlobalSuffixLearnerDFA<I>, GlobalSuffixLearner.GlobalSuffixLearnerMealy<I,O>
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Nested classes/interfaces inherited from interface de.learnlib.algorithm.LearningAlgorithm
LearningAlgorithm.DFALearner<I>, LearningAlgorithm.MealyLearner<I,O>, LearningAlgorithm.MooreLearner<I,O>, LearningAlgorithm.NFALearner<I>
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Nested classes/interfaces inherited from interface de.learnlib.datastructure.observationtable.OTLearner
OTLearner.OTLearnerDFA<I>, OTLearner.OTLearnerMealy<I,O>, OTLearner.OTLearnerMoore<I,O>
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Field Summary
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Fields inherited from class de.learnlib.algorithm.lstar.AbstractExtensibleAutomatonLStar
cexHandler, closingStrategy, initialPrefixes, initialSuffixes
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Fields inherited from class de.learnlib.algorithm.lstar.AbstractAutomatonLStar
internalHyp, stateInfos
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Fields inherited from class de.learnlib.algorithm.lstar.AbstractLStar
alphabet, oracle, table
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Constructor Summary
Constructors Constructor Description ClassicLStarMoore(Alphabet<I> alphabet, MembershipOracle<I,O> oracle, List<Word<I>> initialPrefixes, List<Word<I>> initialSuffixes, ObservationTableCEXHandler<? super I,? super O> cexHandler, ClosingStrategy<? super I,? super O> closingStrategy)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description MooreMachine<?,I,Integer,O>getHypothesisModel()Returns the current hypothesis model.protected SuffixOutput<I,O>hypothesisOutput()protected OstateProperty(ObservationTable<I,O> table, Row<I> stateRow)Derives a state property from the corresponding row.protected VoidtransitionProperty(ObservationTable<I,O> table, Row<I> stateRow, int inputIdx)Derives a transition property from the corresponding transition.-
Methods inherited from class de.learnlib.algorithm.lstar.AbstractExtensibleAutomatonLStar
initialPrefixes, initialSuffixes, refineHypothesisInternal, selectClosingRows
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Methods inherited from class de.learnlib.algorithm.lstar.AbstractAutomatonLStar
addAlphabetSymbol, createState, doRefineHypothesis, resume, setTransition, startLearning, suspend, updateInternalHypothesis
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Methods inherited from class de.learnlib.algorithm.lstar.AbstractLStar
addGlobalSuffixes, analyzeInconsistency, completeConsistentTable, getGlobalSuffixes, getObservationTable, incorporateCounterExample, refineHypothesis
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Method Detail
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getHypothesisModel
public MooreMachine<?,I,Integer,O> getHypothesisModel()
Description copied from interface:LearningAlgorithmReturns the current hypothesis model.N.B.: By the contract of this interface, the model returned by this method may not be modified (i.e., M generally should refer to an immutable interface), and its validity is retained only until the next invocation of
LearningAlgorithm.refineHypothesis(DefaultQuery). If older hypotheses have to be maintained, a copy of the returned model must be made.Please note that it should be illegal to invoke this method before an initial invocation of
LearningAlgorithm.startLearning().- Returns:
- the current hypothesis model.
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stateProperty
protected O stateProperty(ObservationTable<I,O> table, Row<I> stateRow)
Description copied from class:AbstractAutomatonLStarDerives a state property from the corresponding row.- Specified by:
statePropertyin classAbstractAutomatonLStar<MooreMachine<?,I,?,O>,I,O,Integer,Integer,O,Void,CompactMoore<I,O>>- Parameters:
table- the current observation tablestateRow- the row for which the state is created- Returns:
- the state property of the corresponding state
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transitionProperty
protected Void transitionProperty(ObservationTable<I,O> table, Row<I> stateRow, int inputIdx)
Description copied from class:AbstractAutomatonLStarDerives a transition property from the corresponding transition.N.B.: Not the transition row is passed to this method, but the row for the outgoing state. The transition row can be retrieved using
Row.getSuccessor(int).- Specified by:
transitionPropertyin classAbstractAutomatonLStar<MooreMachine<?,I,?,O>,I,O,Integer,Integer,O,Void,CompactMoore<I,O>>stateRow- the row for the source stateinputIdx- the index of the input symbol to consider- Returns:
- the transition property of the corresponding transition
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hypothesisOutput
protected SuffixOutput<I,O> hypothesisOutput()
- Specified by:
hypothesisOutputin classAbstractLStar<MooreMachine<?,I,?,O>,I,O>
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