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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.

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/* Copyright (C) 2013-2023 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.algorithm.lstar.moore;

import java.util.Collections;
import java.util.List;

import com.github.misberner.buildergen.annotations.GenerateBuilder;
import de.learnlib.algorithm.lstar.AbstractExtensibleAutomatonLStar;
import de.learnlib.algorithm.lstar.ce.ObservationTableCEXHandler;
import de.learnlib.algorithm.lstar.closing.ClosingStrategy;
import de.learnlib.datastructure.observationtable.OTLearner.OTLearnerMoore;
import de.learnlib.datastructure.observationtable.ObservationTable;
import de.learnlib.datastructure.observationtable.Row;
import de.learnlib.oracle.MembershipOracle;
import net.automatalib.alphabet.Alphabet;
import net.automatalib.automaton.concept.SuffixOutput;
import net.automatalib.automaton.transducer.CompactMoore;
import net.automatalib.automaton.transducer.MooreMachine;
import net.automatalib.word.Word;

/**
 * A {@link MooreMachine}-based specialization of the extensible L* learner.
 *
 * @param 
 *         input symbol type
 * @param 
 *         output symbol type
 */
public class ExtensibleLStarMoore
        extends AbstractExtensibleAutomatonLStar, I, Word, Integer, Integer, O, Void, CompactMoore>
        implements OTLearnerMoore {

    public ExtensibleLStarMoore(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 ExtensibleLStarMoore(Alphabet alphabet,
                                MembershipOracle> oracle,
                                List> initialPrefixes,
                                List> initialSuffixes,
                                ObservationTableCEXHandler> cexHandler,
                                ClosingStrategy> closingStrategy) {
        super(alphabet,
              oracle,
              new CompactMoore<>(alphabet),
              initialPrefixes,
              LStarMooreUtil.ensureSuffixCompliancy(initialSuffixes),
              cexHandler,
              closingStrategy);
    }

    @Override
    public MooreMachine getHypothesisModel() {
        return internalHyp;
    }

    @Override
    protected O stateProperty(ObservationTable> table, Row stateRow) {
        Word word = table.cellContents(stateRow, 0);
        return word.getSymbol(0);
    }

    @Override
    protected Void transitionProperty(ObservationTable> table, Row stateRow, int inputIdx) {
        return null;
    }

    @Override
    protected SuffixOutput> hypothesisOutput() {
        return internalHyp;
    }
}