13181815301

Committed 06 Feb 2025 03:00PM CUT coverage: 94.368%. Remained the same

Build # 13181815301

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push

github

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mtf90

Commit Message

[maven-release-plugin] prepare for next development iteration

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

/* Copyright (C) 2013-2025 TU Dortmund University
 * This file is part of LearnLib <https://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.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 de.learnlib.tooling.annotation.refinement.GenerateRefinement;
import de.learnlib.tooling.annotation.refinement.Generic;
import de.learnlib.tooling.annotation.refinement.Interface;
import de.learnlib.tooling.annotation.refinement.Mapping;
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>
 *         automaton type
 * @param <I>
 *         input symbol type
 * @param <D>
 *         output domain type
 */
@GenerateRefinement(name = "DFARefinementCounterLearner",
                    generics = @Generic(value = "I", desc = "input symbol type"),
                    parentGenerics = {@Generic(clazz = DFA.class, generics = {"?", "I"}),
                                      @Generic("I"),
                                      @Generic(clazz = Boolean.class)},
                    typeMappings = @Mapping(from = LearningAlgorithm.class,
                                            to = DFALearner.class,
                                            generics = @Generic("I")),
                    interfaces = @Interface(clazz = DFAStatisticLearner.class, generics = @Generic("I")))
@GenerateRefinement(name = "MealyRefinementCounterLearner",
                    generics = {@Generic(value = "I", desc = "input symbol type"),
                                @Generic(value = "O", desc = "output symbol type")},
                    parentGenerics = {@Generic(clazz = MealyMachine.class, generics = {"?", "I", "?", "O"}),
                                      @Generic("I"),
                                      @Generic(clazz = Word.class, generics = "O")},
                    typeMappings = @Mapping(from = LearningAlgorithm.class,
                                            to = MealyLearner.class,
                                            generics = {@Generic("I"), @Generic("O")}),
                    interfaces = @Interface(clazz = MealyStatisticLearner.class,
                                            generics = {@Generic("I"), @Generic("O")}))
@GenerateRefinement(name = "MooreRefinementCounterLearner",
                    generics = {@Generic(value = "I", desc = "input symbol type"),
                                @Generic(value = "O", desc = "output symbol type")},
                    parentGenerics = {@Generic(clazz = MooreMachine.class, generics = {"?", "I", "?", "O"}),
                                      @Generic("I"),
                                      @Generic(clazz = Word.class, generics = "O")},
                    typeMappings = @Mapping(from = LearningAlgorithm.class,
                                            to = MooreLearner.class,
                                            generics = {@Generic("I"), @Generic("O")}),
                    interfaces = @Interface(clazz = MooreStatisticLearner.class,
                                            generics = {@Generic("I"), @Generic("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) 2013-2025 TU Dortmund University
2	* This file is part of LearnLib <https://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.filter.statistic.Counter;
23	import de.learnlib.query.DefaultQuery;
24	import de.learnlib.statistic.StatisticLearner;
25	import de.learnlib.statistic.StatisticLearner.DFAStatisticLearner;
26	import de.learnlib.statistic.StatisticLearner.MealyStatisticLearner;
27	import de.learnlib.statistic.StatisticLearner.MooreStatisticLearner;
28	import de.learnlib.tooling.annotation.refinement.GenerateRefinement;
29	import de.learnlib.tooling.annotation.refinement.Generic;
30	import de.learnlib.tooling.annotation.refinement.Interface;
31	import de.learnlib.tooling.annotation.refinement.Mapping;
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	* automaton type
45	* @param <I>
46	* input symbol type
47	* @param <D>
48	* output domain type
49	*/
50	@GenerateRefinement(name = "DFARefinementCounterLearner",
51	generics = @Generic(value = "I", desc = "input symbol type"),
52	parentGenerics = {@Generic(clazz = DFA.class, generics = {"?", "I"}),
53	@Generic("I"),
54	@Generic(clazz = Boolean.class)},
55	typeMappings = @Mapping(from = LearningAlgorithm.class,
56	to = DFALearner.class,
57	generics = @Generic("I")),
58	interfaces = @Interface(clazz = DFAStatisticLearner.class, generics = @Generic("I")))
59	@GenerateRefinement(name = "MealyRefinementCounterLearner",
60	generics = {@Generic(value = "I", desc = "input symbol type"),
61	@Generic(value = "O", desc = "output symbol type")},
62	parentGenerics = {@Generic(clazz = MealyMachine.class, generics = {"?", "I", "?", "O"}),
63	@Generic("I"),
64	@Generic(clazz = Word.class, generics = "O")},
65	typeMappings = @Mapping(from = LearningAlgorithm.class,
66	to = MealyLearner.class,
67	generics = {@Generic("I"), @Generic("O")}),
68	interfaces = @Interface(clazz = MealyStatisticLearner.class,
69	generics = {@Generic("I"), @Generic("O")}))
70	@GenerateRefinement(name = "MooreRefinementCounterLearner",
71	generics = {@Generic(value = "I", desc = "input symbol type"),
72	@Generic(value = "O", desc = "output symbol type")},
73	parentGenerics = {@Generic(clazz = MooreMachine.class, generics = {"?", "I", "?", "O"}),
74	@Generic("I"),
75	@Generic(clazz = Word.class, generics = "O")},
76	typeMappings = @Mapping(from = LearningAlgorithm.class,
77	to = MooreLearner.class,
78	generics = {@Generic("I"), @Generic("O")}),
79	interfaces = @Interface(clazz = MooreStatisticLearner.class,
80	generics = {@Generic("I"), @Generic("O")}))
81	public class RefinementCounterLearner<M, I, D> implements StatisticLearner<M, I, D> {
82
83	private final LearningAlgorithm<M, I, D> learningAlgorithm;
84
85	private final Counter counter;
86
87	public RefinementCounterLearner(LearningAlgorithm<M, I, D> learningAlgorithm) {	×
88	this.learningAlgorithm = learningAlgorithm;	×
89	this.counter = new Counter("Refinements", "#");	×
90	}	×
91
92	@Override
93	public void startLearning() {
94	learningAlgorithm.startLearning();	×
95	}	×
96
97	@Override
98	public boolean refineHypothesis(DefaultQuery<I, D> ceQuery) {
99	final boolean refined = learningAlgorithm.refineHypothesis(ceQuery);	×
100	if (refined) {	×
101	counter.increment();	×
102	}
103	return refined;	×
104	}
105
106	@Override
107	public M getHypothesisModel() {
108	return learningAlgorithm.getHypothesisModel();	×
109	}
110
111	@Override
112	public Counter getStatisticalData() {
113	return counter;	×
114	}
115	}

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