Class GenericAAARLearnerDFA<L extends LearningAlgorithm.DFALearner<CI> & SupportsGrowingAlphabet<CI>,AI,CI>
- java.lang.Object
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- de.learnlib.algorithm.aaar.AbstractAAARLearner<L,AM,CM,AI,CI,D>
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- de.learnlib.algorithm.aaar.generic.AbstractGenericAAARLearner<L,DFA<?,AI>,DFA<?,CI>,AI,CI,Boolean>
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- de.learnlib.algorithm.aaar.generic.GenericAAARLearnerDFA<L,AI,CI>
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- Type Parameters:
L
- learner typeAI
- abstract input symbol typeCI
- concrete input symbol type
- All Implemented Interfaces:
LearningAlgorithm<DFA<?,AI>,CI,Boolean>
public class GenericAAARLearnerDFA<L extends LearningAlgorithm.DFALearner<CI> & SupportsGrowingAlphabet<CI>,AI,CI> extends AbstractGenericAAARLearner<L,DFA<?,AI>,DFA<?,CI>,AI,CI,Boolean>
ADFA
-specific refinement ofAbstractGenericAAARLearner
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Nested Class Summary
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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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Constructor Summary
Constructors Constructor Description GenericAAARLearnerDFA(LearnerConstructor<L,CI,Boolean> learnerConstructor, MembershipOracle<CI,Boolean> oracle, CI initialConcrete, Function<CI,AI> abstractor)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description DFA<?,AI>
getHypothesisModel()
Returns the current hypothesis model.DFA<?,CI>
getTranslatingHypothesisModel()
Returns a model of the current internal hypothesis model (cf.-
Methods inherited from class de.learnlib.algorithm.aaar.generic.AbstractGenericAAARLearner
getAbstractionTree, getInitialAbstracts, getInitialRepresentatives, getLearnerAlphabet, getTreeForRepresentative
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Methods inherited from class de.learnlib.algorithm.aaar.AbstractAAARLearner
copyAbstract, getAbstractAlphabet, getLearner, getLearnerHypothesisModel, refineHypothesis, startLearning
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Constructor Detail
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GenericAAARLearnerDFA
public GenericAAARLearnerDFA(LearnerConstructor<L,CI,Boolean> learnerConstructor, MembershipOracle<CI,Boolean> oracle, CI initialConcrete, Function<CI,AI> abstractor)
Constructor.- Parameters:
learnerConstructor
- the provider for constructing the internal (concrete) learneroracle
- the (concrete) membership oracleinitialConcrete
- the initial (concrete) input symbol used for starting the learning processabstractor
- the function for creating new abstract input symbols given concrete one. This function only receives input symbols from the provided (concrete) counterexamples
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Method Detail
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getHypothesisModel
public DFA<?,AI> getHypothesisModel()
Description copied from interface:LearningAlgorithm
Returns 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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getTranslatingHypothesisModel
public DFA<?,CI> getTranslatingHypothesisModel()
Description copied from class:AbstractAAARLearner
Returns a model of the current internal hypothesis model (cf.AbstractAAARLearner.getLearnerHypothesisModel()
) that automatically transforms (concrete) input symbols to abstract ones and uses their representatives to actually perform transitions. This allows the returned model to handle (concrete) input symbols that have not yet been added to the hypothesis by previous abstraction refinements. Note that this model requires theMembershipOracle
passed to the constructor of this learner to still function in order to determine the abstract input symbols.- Specified by:
getTranslatingHypothesisModel
in classAbstractAAARLearner<L extends LearningAlgorithm.DFALearner<CI> & SupportsGrowingAlphabet<CI>,DFA<?,AI>,DFA<?,CI>,AI,CI,Boolean>
- Returns:
- the (concrete) hypothesis model that automatically transforms input symbols
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