de.learnlib.algorithms.lstar.dfa.ExtensibleLStarDFA Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of learnlib-lstar Show documentation
Show all versions of learnlib-lstar Show documentation
This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular
Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including
variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular
Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata
using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.
/* Copyright (C) 2013-2018 TU Dortmund
* This file is part of LearnLib, http://www.learnlib.de/.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package de.learnlib.algorithms.lstar.dfa;
import java.util.Collections;
import java.util.List;
import com.github.misberner.buildergen.annotations.GenerateBuilder;
import de.learnlib.algorithms.lstar.AbstractExtensibleAutomatonLStar;
import de.learnlib.algorithms.lstar.ce.ObservationTableCEXHandler;
import de.learnlib.algorithms.lstar.closing.ClosingStrategy;
import de.learnlib.api.oracle.MembershipOracle;
import de.learnlib.datastructure.observationtable.OTLearner.OTLearnerDFA;
import de.learnlib.datastructure.observationtable.ObservationTable;
import de.learnlib.datastructure.observationtable.Row;
import net.automatalib.automata.concepts.SuffixOutput;
import net.automatalib.automata.fsa.DFA;
import net.automatalib.automata.fsa.impl.compact.CompactDFA;
import net.automatalib.words.Alphabet;
import net.automatalib.words.Word;
/**
* An implementation of Angluin's L* algorithm for learning DFAs, as described in the paper "Learning Regular Sets from
* Queries and Counterexamples".
*
* @param
* input symbol class.
*
* @author Malte Isberner
*/
public class ExtensibleLStarDFA
extends AbstractExtensibleAutomatonLStar, I, Boolean, Integer, Integer, Boolean, Void, CompactDFA>
implements OTLearnerDFA {
/**
* Constructor.
*
* @param alphabet
* the learning alphabet.
* @param oracle
* the DFA oracle.
*/
public ExtensibleLStarDFA(Alphabet alphabet,
MembershipOracle oracle,
List> initialSuffixes,
ObservationTableCEXHandler cexHandler,
ClosingStrategy closingStrategy) {
this(alphabet, oracle, Collections.singletonList(Word.epsilon()), initialSuffixes, cexHandler, closingStrategy);
}
@GenerateBuilder(defaults = AbstractExtensibleAutomatonLStar.BuilderDefaults.class)
public ExtensibleLStarDFA(Alphabet alphabet,
MembershipOracle oracle,
List> initialPrefixes,
List> initialSuffixes,
ObservationTableCEXHandler cexHandler,
ClosingStrategy closingStrategy) {
super(alphabet,
oracle,
new CompactDFA<>(alphabet),
initialPrefixes,
LStarDFAUtil.ensureSuffixCompliancy(initialSuffixes),
cexHandler,
closingStrategy);
}
@Override
protected List> initialSuffixes() {
return Collections.singletonList(Word.epsilon());
}
@Override
protected DFA exposeInternalHypothesis() {
return internalHyp;
}
@Override
protected Boolean stateProperty(ObservationTable table, Row stateRow) {
return table.cellContents(stateRow, 0);
}
@Override
protected Void transitionProperty(ObservationTable table, Row stateRow, int inputIdx) {
return null;
}
@Override
protected SuffixOutput hypothesisOutput() {
return internalHyp;
}
}