List of learnMachine() Examples

Examples of learnMachine()

statechum.analysis.learning.ErlangOracleLearner.learnMachine()
statechum.analysis.learning.Learner.learnMachine()
Learns the machine. When different learning events occur, the learner calls its listeners (which are the decorators); these listeners are given a chance to inspect the arguments, do something and pass the call further or not pass. At the bottom level, there is default processing performed by the learner itself.
For internal use only. @param topLevelDecorator the first listener in a chain of them.
statechum.analysis.learning.RPNIBlueFringeLearnerTestComponentOpt.learnMachine()
statechum.analysis.learning.RPNIUniversalLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.Cav2014.KTailsReferenceLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.Cav2014.LearnerMarkovPassive.learnMachine()
statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.LearnerThatCanClassifyPairs.learnMachine()
statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.ReferenceLearner.learnMachine()
statechum.analysis.learning.observers.AutoAnswers.learnMachine()
statechum.analysis.learning.observers.Learner.learnMachine()
Learns the machine. When different learning events occur, the learner calls its listeners (which are the decorators); these listeners are given a chance to inspect the arguments, do something and pass the call further or not pass. At the bottom level, there is default processing performed by the learner itself.
For internal use only. @param topLevelDecorator the first listener in a chain of them.
statechum.analysis.learning.observers.LearningConvergenceObserver.learnMachine()
statechum.analysis.learning.observers.QuestionAndRestartCounter.learnMachine()
statechum.analysis.learning.observers.RecordProgressDecorator.learnMachine()

Examples of statechum.analysis.learning.ErlangOracleLearner.learnMachine()

                              
                            };
                            if (learnerInitConfiguration.config.getAskQuestions()) // only generate initial traces if we are permited to ask questions.
                              learner.init(learner.GenerateInitialTraces(learnerInitConfiguration.config.getErlangInitialTraceLength()),0,0);
                            System.out.println("random trace generation complete");
                            LearnerGraph graphLearnt = learner.learnMachine(),
                                graphWithTrimmedLabels = new LearnerGraph(learnerInitConfiguration.config);
                            
                            if (learnerInitConfiguration.config.getErlangStripModuleNamesFromFunctionsInNonGenModules())
                              convertLabelsToStrings(graphLearnt,graphWithTrimmedLabels);
                            else

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Examples of statechum.analysis.learning.ErlangOracleLearner.learnMachine()

                              
                            };
                            if (learnerInitConfiguration.config.getAskQuestions()) // only generate initial traces if we are permited to ask questions.
                              learner.init(learner.GenerateInitialTraces(learnerInitConfiguration.config.getErlangInitialTraceLength()),0,0);
                            System.out.println("random trace generation complete");
                            LearnerGraph graphLearnt = learner.learnMachine(),
                                graphWithTrimmedLabels = new LearnerGraph(learnerInitConfiguration.config);
                            
                            if (learnerInitConfiguration.config.getErlangStripModuleNamesFromFunctionsInNonGenModules())
                              convertLabelsToStrings(graphLearnt,graphWithTrimmedLabels);
                            else

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Examples of statechum.analysis.learning.ErlangOracleLearner.learnMachine()

          ErlangOracleVisualiser viz = new ErlangOracleVisualiser();
          ErlangOracleLearner innerLearner = ErlangQSMOracle.createLearner(viz,tracefile);
          innerLearner.addObserver(viz);
          innerLearner.setGraphNameSuffix(new File(tracefile).getName());
          innerLearner.getTentativeAutomaton().getLayoutOptions().showNegatives = false;
          LearnerGraph g = innerLearner.learnMachine();
          
          System.out.println("Produced " + g.getStateNumber() + " states");
        } catch (Exception e) {
          JOptionPane.showMessageDialog(this, e.toString());
          e.printStackTrace();

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Examples of statechum.analysis.learning.Learner.learnMachine()

    config.setDebugMode(false);
    //l.setPairsMergedPerHypothesis(0);
    //l.setGeneralisationThreshold(1);
    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);

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Examples of statechum.analysis.learning.Learner.learnMachine()

    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }

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Examples of statechum.analysis.learning.Learner.learnMachine()

    config.setDebugMode(false);
    //l.setPairsMergedPerHypothesis(0);
    //l.setGeneralisationThreshold(1);
    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);

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Examples of statechum.analysis.learning.Learner.learnMachine()

    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }

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Examples of statechum.analysis.learning.Learner.learnMachine()

    config.setDebugMode(false);
    //l.setPairsMergedPerHypothesis(0);
    //l.setGeneralisationThreshold(1);
    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);

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Examples of statechum.analysis.learning.Learner.learnMachine()

    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }

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Examples of statechum.analysis.learning.Learner.learnMachine()

    config.setDebugMode(false);
    //l.setPairsMergedPerHypothesis(0);
    //l.setGeneralisationThreshold(1);
    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus,testConfig,getLabelConverter()), buildSet(minus,testConfig,getLabelConverter())),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus,testConfig,getLabelConverter()));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus,testConfig,getLabelConverter()));
    LearnerGraph learntStructureB = new LearnerGraph(l.learnMachine(plusPTA, minusPTA),expected.config);
    Assert.assertNull(WMethod.checkM(learntStructureA, learntStructureB));
    LearnerGraph learntMachineNoRejects = new LearnerGraph(expected.config);

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