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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.mealy;
import java.util.ArrayList;
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.ObservationTable;
import de.learnlib.datastructure.observationtable.Row;
import de.learnlib.util.mealy.MealyUtil;
import net.automatalib.automata.concepts.SuffixOutput;
import net.automatalib.automata.transout.MealyMachine;
import net.automatalib.automata.transout.MutableMealyMachine;
import net.automatalib.automata.transout.impl.compact.CompactMealy;
import net.automatalib.automata.transout.impl.compact.CompactMealyTransition;
import net.automatalib.words.Alphabet;
import net.automatalib.words.Word;
/**
* An implementation of the L*Mealy algorithm for inferring Mealy machines, as described by Oliver Niese in his Ph.D.
* thesis.
*
* @param
* input symbol class
* @param
* output symbol class
*
* @author Malte Isberner
*/
public class ClassicLStarMealy
extends AbstractExtensibleAutomatonLStar, I, O, Integer, CompactMealyTransition, Void, O, CompactMealy> {
/**
* Constructor.
*
* @param alphabet
* the learning alphabet
* @param oracle
* the (Mealy) oracle
*/
public ClassicLStarMealy(Alphabet alphabet,
MembershipOracle oracle,
ObservationTableCEXHandler super I, ? super O> cexHandler,
ClosingStrategy super I, ? super O> closingStrategy) {
this(alphabet,
oracle,
Collections.singletonList(Word.epsilon()),
Collections.emptyList(),
cexHandler,
closingStrategy);
}
@GenerateBuilder(defaults = AbstractExtensibleAutomatonLStar.BuilderDefaults.class)
public ClassicLStarMealy(Alphabet alphabet,
MembershipOracle oracle,
List> initialPrefixes,
List> initialSuffixes,
ObservationTableCEXHandler super I, ? super O> cexHandler,
ClosingStrategy super I, ? super O> closingStrategy) {
super(alphabet,
oracle,
new CompactMealy<>(alphabet),
initialPrefixes,
LStarMealyUtil.ensureSuffixCompliancy(initialSuffixes, alphabet, true),
cexHandler,
closingStrategy);
}
public static , I, O> ClassicLStarMealy createForSymbolOracle(
Alphabet alphabet,
MembershipOracle oracle,
ObservationTableCEXHandler cexHandler,
ClosingStrategy super I, ? super O> closingStrategy) {
return new ClassicLStarMealy<>(alphabet, oracle, cexHandler, closingStrategy);
}
public static , I, O> ClassicLStarMealy createForWordOracle(Alphabet alphabet,
MembershipOracle> oracle,
ObservationTableCEXHandler super I, ? super O> cexHandler,
ClosingStrategy super I, ? super O> closingStrategy) {
return new ClassicLStarMealy<>(alphabet, MealyUtil.wrapWordOracle(oracle), cexHandler, closingStrategy);
}
@Override
protected List> initialSuffixes() {
List> suffixes = new ArrayList<>(alphabet.size());
for (int i = 0; i < alphabet.size(); i++) {
I sym = alphabet.getSymbol(i);
suffixes.add(Word.fromLetter(sym));
}
return suffixes;
}
@Override
protected MealyMachine, I, ?, O> exposeInternalHypothesis() {
return internalHyp;
}
@Override
protected Void stateProperty(ObservationTable table, Row stateRow) {
return null;
}
@Override
protected O transitionProperty(ObservationTable table, Row stateRow, int inputIdx) {
return table.cellContents(stateRow, inputIdx);
}
@Override
protected SuffixOutput hypothesisOutput() {
return new SuffixOutput() {
@Override
public O computeOutput(Iterable extends I> input) {
return computeSuffixOutput(Collections.emptyList(), input);
}
@Override
public O computeSuffixOutput(Iterable extends I> prefix, Iterable extends I> suffix) {
Word wordOut = internalHyp.computeSuffixOutput(prefix, suffix);
if (wordOut.isEmpty()) {
return null;
}
return wordOut.lastSymbol();
}
};
}
}