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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) 2013-2018 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 cexHandler,
                             ClosingStrategy 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 cexHandler,
                             ClosingStrategy 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 closingStrategy) {
        return new ClassicLStarMealy<>(alphabet, oracle, cexHandler, closingStrategy);
    }

    public static , I, O> ClassicLStarMealy createForWordOracle(Alphabet alphabet,
                                                                                                                MembershipOracle> oracle,
                                                                                                                ObservationTableCEXHandler cexHandler,
                                                                                                                ClosingStrategy 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 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 input) {
                return computeSuffixOutput(Collections.emptyList(), input);
            }

            @Override
            public O computeSuffixOutput(Iterable prefix, Iterable suffix) {
                Word wordOut = internalHyp.computeSuffixOutput(prefix, suffix);
                if (wordOut.isEmpty()) {
                    return null;
                }
                return wordOut.lastSymbol();
            }

        };
    }

}