de.learnlib.algorithms.baselinelstar

Class BaselineLStar<I>

java.lang.Object

de.learnlib.algorithms.baselinelstar.BaselineLStar<I>

Type Parameters:

I - input symbol class.

All Implemented Interfaces:

LearningAlgorithm<DFA<?,I>,I,Boolean>, LearningAlgorithm.DFALearner<I>

public class BaselineLStar<I>
extends Object
implements LearningAlgorithm.DFALearner<I>

Implementation of the L* algorithm by Dana Angluin

Nested Class Summary

Nested classes/interfaces inherited from interface de.learnlib.api.LearningAlgorithm

LearningAlgorithm.DFALearner<I>, LearningAlgorithm.MealyLearner<I,O>

Constructor Summary

Constructors

Constructor and Description
BaselineLStar(Alphabet<I> alphabet, MembershipOracle<I,Boolean> oracle) Initializes a newly created baseline L* implementation.

Method Summary

Methods

Modifier and Type	Method and Description
DFA<?,I>	getHypothesisModel() Returns the current hypothesis model.
String	getStringRepresentationOfObservationTable()
boolean	refineHypothesis(DefaultQuery<I,Boolean> ceQuery) Triggers a refinement of the model by providing a counterexample.
void	startLearning() Starts the model inference process, creating an initial hypothesis in the provided model object.

Methods inherited from class java.lang.Object

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

Constructor Detail

BaselineLStar

public BaselineLStar(Alphabet<I> alphabet,
             MembershipOracle<I,Boolean> oracle)

Initializes a newly created baseline L* implementation. After this, the method startLearning() may be called once.

Parameters:

alphabet - The Alphabet to learn.

oracle - The MembershipOracle which is used for membership queries.

Method Detail

startLearning

public void startLearning()

Description copied from interface: LearningAlgorithm

Starts the model inference process, creating an initial hypothesis in the provided model object. Please note that it should be illegal to invoke this method twice.

Specified by:

startLearning in interface LearningAlgorithm<DFA<?,I>,I,Boolean>

refineHypothesis

public boolean refineHypothesis(DefaultQuery<I,Boolean> ceQuery)

Description copied from interface: LearningAlgorithm

Triggers a refinement of the model by providing a counterexample. A counterexample is a query which exposes different behavior of the real SUL compared to the hypothesis. Please note that invoking this method before an initial invocation of LearningAlgorithm.startLearning() should be illegal.

Specified by:

refineHypothesis in interface LearningAlgorithm<DFA<?,I>,I,Boolean>

Parameters:

ceQuery - the query which exposes diverging behavior, as posed to the real SUL (i.e. with the SULs output).

Returns:

true if the counterexample triggered a refinement of the hypothesis, false otherwise (i.e., it was no counterexample).

getHypothesisModel

public DFA<?,I> 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().

Specified by:

getHypothesisModel in interface LearningAlgorithm<DFA<?,I>,I,Boolean>

Returns:

the current hypothesis model.

getStringRepresentationOfObservationTable

public String getStringRepresentationOfObservationTable()