6877022493

Committed 15 Nov 2023 12:04PM CUT coverage: 93.36%. First build

Build # 6877022493

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push

github

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mtf90

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[maven-release-plugin] prepare release learnlib-0.17.0

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/filters/statistics/src/main/java/de/learnlib/filter/statistic/learner/RefinementCounterLearner.java

/* 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.filter.statistic.learner;

import de.learnlib.algorithm.LearningAlgorithm;
import de.learnlib.algorithm.LearningAlgorithm.DFALearner;
import de.learnlib.algorithm.LearningAlgorithm.MealyLearner;
import de.learnlib.algorithm.LearningAlgorithm.MooreLearner;
import de.learnlib.buildtool.refinement.annotation.GenerateRefinement;
import de.learnlib.buildtool.refinement.annotation.Generic;
import de.learnlib.buildtool.refinement.annotation.Interface;
import de.learnlib.buildtool.refinement.annotation.Map;
import de.learnlib.filter.statistic.Counter;
import de.learnlib.query.DefaultQuery;
import de.learnlib.statistic.StatisticLearner;
import de.learnlib.statistic.StatisticLearner.DFAStatisticLearner;
import de.learnlib.statistic.StatisticLearner.MealyStatisticLearner;
import de.learnlib.statistic.StatisticLearner.MooreStatisticLearner;
import net.automatalib.automaton.fsa.DFA;
import net.automatalib.automaton.transducer.MealyMachine;
import net.automatalib.automaton.transducer.MooreMachine;
import net.automatalib.word.Word;

/**
 * Counts the number of hypothesis refinements.
 * <p>
 * The value of the {@link Counter} returned by {@link #getStatisticalData()} returns the same value as
 * Experiment.getRounds().
 *
 * @param <M>
 *         the automaton type.
 * @param <I>
 *         the input type.
 * @param <D>
 *         the output type.
 */
@GenerateRefinement(name = "DFARefinementCounterLearner",
                    generics = "I",
                    parentGenerics = {@Generic(clazz = DFA.class, generics = {"?", "I"}),
                                      @Generic("I"),
                                      @Generic(clazz = Boolean.class)},
                    parameterMapping = @Map(from = LearningAlgorithm.class, to = DFALearner.class, withGenerics = "I"),
                    interfaces = @Interface(clazz = DFAStatisticLearner.class, generics = "I"))
@GenerateRefinement(name = "MealyRefinementCounterLearner",
                    generics = {"I", "O"},
                    parentGenerics = {@Generic(clazz = MealyMachine.class, generics = {"?", "I", "?", "O"}),
                                      @Generic("I"),
                                      @Generic(clazz = Word.class, generics = "O")},
                    parameterMapping = @Map(from = LearningAlgorithm.class,
                                            to = MealyLearner.class,
                                            withGenerics = {"I", "O"}),
                    interfaces = @Interface(clazz = MealyStatisticLearner.class, generics = {"I", "O"}))
@GenerateRefinement(name = "MooreRefinementCounterLearner",
                    generics = {"I", "O"},
                    parentGenerics = {@Generic(clazz = MooreMachine.class, generics = {"?", "I", "?", "O"}),
                                      @Generic("I"),
                                      @Generic(clazz = Word.class, generics = "O")},
                    parameterMapping = @Map(from = LearningAlgorithm.class,
                                            to = MooreLearner.class,
                                            withGenerics = {"I", "O"}),
                    interfaces = @Interface(clazz = MooreStatisticLearner.class, generics = {"I", "O"}))
public class RefinementCounterLearner<M, I, D> implements StatisticLearner<M, I, D> {

    private final LearningAlgorithm<M, I, D> learningAlgorithm;

    private final Counter counter;

    public RefinementCounterLearner(LearningAlgorithm<M, I, D> learningAlgorithm) {
        this.learningAlgorithm = learningAlgorithm;
        this.counter = new Counter("Refinements", "#");
    }

    @Override
    public void startLearning() {
        learningAlgorithm.startLearning();
    }

    @Override
    public boolean refineHypothesis(DefaultQuery<I, D> ceQuery) {
        final boolean refined = learningAlgorithm.refineHypothesis(ceQuery);
        if (refined) {
            counter.increment();
        }
        return refined;
    }

    @Override
    public M getHypothesisModel() {
        return learningAlgorithm.getHypothesisModel();
    }

    @Override
    public Counter getStatisticalData() {
        return counter;
    }
}

1	/* Copyright (C) 2013-2023 TU Dortmund
2	* This file is part of LearnLib, http://www.learnlib.de/.
3	*
4	* Licensed under the Apache License, Version 2.0 (the "License");
5	* you may not use this file except in compliance with the License.
6	* You may obtain a copy of the License at
7	*
8	* http://www.apache.org/licenses/LICENSE-2.0
9	*
10	* Unless required by applicable law or agreed to in writing, software
11	* distributed under the License is distributed on an "AS IS" BASIS,
12	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	* See the License for the specific language governing permissions and
14	* limitations under the License.
15	*/
16	package de.learnlib.filter.statistic.learner;
17
18	import de.learnlib.algorithm.LearningAlgorithm;
19	import de.learnlib.algorithm.LearningAlgorithm.DFALearner;
20	import de.learnlib.algorithm.LearningAlgorithm.MealyLearner;
21	import de.learnlib.algorithm.LearningAlgorithm.MooreLearner;
22	import de.learnlib.buildtool.refinement.annotation.GenerateRefinement;
23	import de.learnlib.buildtool.refinement.annotation.Generic;
24	import de.learnlib.buildtool.refinement.annotation.Interface;
25	import de.learnlib.buildtool.refinement.annotation.Map;
26	import de.learnlib.filter.statistic.Counter;
27	import de.learnlib.query.DefaultQuery;
28	import de.learnlib.statistic.StatisticLearner;
29	import de.learnlib.statistic.StatisticLearner.DFAStatisticLearner;
30	import de.learnlib.statistic.StatisticLearner.MealyStatisticLearner;
31	import de.learnlib.statistic.StatisticLearner.MooreStatisticLearner;
32	import net.automatalib.automaton.fsa.DFA;
33	import net.automatalib.automaton.transducer.MealyMachine;
34	import net.automatalib.automaton.transducer.MooreMachine;
35	import net.automatalib.word.Word;
36
37	/**
38	* Counts the number of hypothesis refinements.
39	* <p>
40	* The value of the {@link Counter} returned by {@link #getStatisticalData()} returns the same value as
41	* Experiment.getRounds().
42	*
43	* @param <M>
44	* the automaton type.
45	* @param <I>
46	* the input type.
47	* @param <D>
48	* the output type.
49	*/
50	@GenerateRefinement(name = "DFARefinementCounterLearner",
51	generics = "I",
52	parentGenerics = {@Generic(clazz = DFA.class, generics = {"?", "I"}),
53	@Generic("I"),
54	@Generic(clazz = Boolean.class)},
55	parameterMapping = @Map(from = LearningAlgorithm.class, to = DFALearner.class, withGenerics = "I"),
56	interfaces = @Interface(clazz = DFAStatisticLearner.class, generics = "I"))
57	@GenerateRefinement(name = "MealyRefinementCounterLearner",
58	generics = {"I", "O"},
59	parentGenerics = {@Generic(clazz = MealyMachine.class, generics = {"?", "I", "?", "O"}),
60	@Generic("I"),
61	@Generic(clazz = Word.class, generics = "O")},
62	parameterMapping = @Map(from = LearningAlgorithm.class,
63	to = MealyLearner.class,
64	withGenerics = {"I", "O"}),
65	interfaces = @Interface(clazz = MealyStatisticLearner.class, generics = {"I", "O"}))
66	@GenerateRefinement(name = "MooreRefinementCounterLearner",
67	generics = {"I", "O"},
68	parentGenerics = {@Generic(clazz = MooreMachine.class, generics = {"?", "I", "?", "O"}),
69	@Generic("I"),
70	@Generic(clazz = Word.class, generics = "O")},
71	parameterMapping = @Map(from = LearningAlgorithm.class,
72	to = MooreLearner.class,
73	withGenerics = {"I", "O"}),
74	interfaces = @Interface(clazz = MooreStatisticLearner.class, generics = {"I", "O"}))
75	public class RefinementCounterLearner<M, I, D> implements StatisticLearner<M, I, D> {
76
77	private final LearningAlgorithm<M, I, D> learningAlgorithm;
78
79	private final Counter counter;
80
81	public RefinementCounterLearner(LearningAlgorithm<M, I, D> learningAlgorithm) {	×
82	this.learningAlgorithm = learningAlgorithm;	×
83	this.counter = new Counter("Refinements", "#");	×
84	}	×
85
86	@Override
87	public void startLearning() {
88	learningAlgorithm.startLearning();	×
89	}	×
90
91	@Override
92	public boolean refineHypothesis(DefaultQuery<I, D> ceQuery) {
93	final boolean refined = learningAlgorithm.refineHypothesis(ceQuery);	×
94	if (refined) {	×
95	counter.increment();	×
96	}
97	return refined;	×
98	}
99
100	@Override
101	public M getHypothesisModel() {
102	return learningAlgorithm.getHypothesisModel();	×
103	}
104
105	@Override
106	public Counter getStatisticalData() {
107	return counter;	×
108	}
109	}

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