de.learnlib.algorithms.lstargeneric

Class AbstractAutomatonLStar<A,I,D,S,T,SP,TP,AI extends MutableDeterministic<S,I,T,SP,TP>>

java.lang.Object

de.learnlib.algorithms.lstargeneric.AbstractLStar<A,I,D>

de.learnlib.algorithms.lstargeneric.AbstractAutomatonLStar<A,I,D,S,T,SP,TP,AI>

Type Parameters:

A - automaton type, must be a subclass of MutableDeterministic

I - input symbol type

D - output domain type

SP - state property type

TP - transition property type

All Implemented Interfaces:

GlobalSuffixFeature<I>, GlobalSuffixLearner<A,I,D>, ObservationTableFeature<I,D>, OTLearner<A,I,D>, LearningAlgorithm<A,I,D>

Direct Known Subclasses:

ExtensibleAutomatonLStar

public abstract class AbstractAutomatonLStar<A,I,D,S,T,SP,TP,AI extends MutableDeterministic<S,I,T,SP,TP>>
extends AbstractLStar<A,I,D>

Abstract base class for algorithms that produce (subclasses of) MutableDeterministic automata. This class provides the L*-style hypothesis construction. Implementing classes solely have to specify how state and transition properties should be derived.

Author:

Malte Isberner

Nested Class Summary

Nested classes/interfaces inherited from interface de.learnlib.algorithms.features.observationtable.OTLearner

OTLearner.OTLearnerDFA<I>, OTLearner.OTLearnerMealy<I,O>

Nested classes/interfaces inherited from interface de.learnlib.algorithms.features.globalsuffixes.GlobalSuffixLearner

GlobalSuffixLearner.GlobalSuffixLearnerDFA<I>, GlobalSuffixLearner.GlobalSuffixLearnerMealy<I,O>

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

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

Field Summary

Fields

Modifier and Type	Field and Description
protected AI	internalHyp
protected ArrayList<de.learnlib.algorithms.lstargeneric.AbstractAutomatonLStar.StateInfo<S,I>>	stateInfos

Fields inherited from class de.learnlib.algorithms.lstargeneric.AbstractLStar

alphabet, oracle, table

Constructor Summary

Constructors

Constructor and Description
AbstractAutomatonLStar(Alphabet<I> alphabet, MembershipOracle<I,D> oracle, AI internalHyp) Constructor.

Method Summary

Modifier and Type	Method and Description
protected S	createState(boolean initial, Row<I> row)
protected void	doRefineHypothesis(DefaultQuery<I,D> ceQuery)
protected abstract A	exposeInternalHypothesis()
A	getHypothesisModel() Returns the current hypothesis model.
protected void	refineHypothesisInternal(DefaultQuery<I,D> ceQuery)
protected void	setTransition(S from, I input, S to, Row<I> fromRow, int inputIdx, Row<I> toRow)
void	startLearning() Starts the model inference process, creating an initial hypothesis in the provided model object.
protected abstract SP	stateProperty(Row<I> stateRow) Derives a state property from the corresponding row.
protected abstract TP	transitionProperty(Row<I> stateRow, int inputIdx) Derives a transition property from the corresponding transition.
protected void	updateInternalHypothesis() Performs the L*-style hypothesis construction.

Methods inherited from class de.learnlib.algorithms.lstargeneric.AbstractLStar

addGlobalSuffixes, analyzeInconsistency, completeConsistentTable, getGlobalSuffixes, getObservationTable, hypothesisOutput, incorporateCounterExample, initialPrefixes, initialSuffixes, refineHypothesis, selectClosingRows

Methods inherited from class java.lang.Object

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

Field Detail

internalHyp

protected final AI extends MutableDeterministic<S,I,T,SP,TP> internalHyp

stateInfos

protected final ArrayList<de.learnlib.algorithms.lstargeneric.AbstractAutomatonLStar.StateInfo<S,I>> stateInfos

Constructor Detail

AbstractAutomatonLStar

public AbstractAutomatonLStar(Alphabet<I> alphabet,
                              MembershipOracle<I,D> oracle,
                              AI internalHyp)

Constructor.

Parameters:

alphabet - the learning alphabet

oracle - the learning oracle

Method Detail

stateProperty

protected abstract SP stateProperty(Row<I> stateRow)

Derives a state property from the corresponding row.

Parameters:

stateRow - the row for which the state is created

Returns:

the state property of the corresponding state

transitionProperty

protected abstract TP transitionProperty(Row<I> stateRow,
                                         int inputIdx)

Derives 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).

Parameters:

stateRow - the row for the source state

inputIdx - the index of the input symbol to consider

Returns:

the transition property of the corresponding transition

exposeInternalHypothesis

protected abstract A exposeInternalHypothesis()

updateInternalHypothesis

protected void updateInternalHypothesis()

Performs the L*-style hypothesis construction. For creating states and transitions, the stateProperty(Row) and transitionProperty(Row, int) methods are used to derive the respective properties.

createState

protected S createState(boolean initial,
                        Row<I> row)

setTransition

protected void setTransition(S from,
                             I input,
                             S to,
                             Row<I> fromRow,
                             int inputIdx,
                             Row<I> toRow)

getHypothesisModel

public A 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.

startLearning

public final 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<A,I,D>

Overrides:

startLearning in class AbstractLStar<A,I,D>

doRefineHypothesis

protected final void doRefineHypothesis(DefaultQuery<I,D> ceQuery)

Overrides:

doRefineHypothesis in class AbstractLStar<A,I,D>

refineHypothesisInternal

protected void refineHypothesisInternal(DefaultQuery<I,D> ceQuery)