Package statechum.analysis.learning

Examples of statechum.analysis.learning.Learner

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    for(String [] path:plus)
      assert AbstractOracle.USER_ACCEPTED == expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    for(String [] path:minus)
      assert AbstractOracle.USER_ACCEPTED != expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    // Visualiser.getVisualiser()
    Learner l = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,testConfig,null,null))
    {
      @Override
      public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
      {
        // Check that compatibility score computation gives the same response as if we did merge and computed a difference between the number of states.
        ScoreMode origScore = original.config.getLearnerScoreMode();original.config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);
        long compatibilityScore = original.pairscores.computePairCompatibilityScore(pair);
        original.config.setLearnerScoreMode(origScore);
        LearnerGraph outcome = super.MergeAndDeterminize(original, pair);
        Assert.assertEquals(compatibilityScore+1,original.getStateNumber()-outcome.getStateNumber());
        return outcome;
      }

      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
          @SuppressWarnings("unused") List<Boolean> acceptedElements,
          @SuppressWarnings("unused") PairScore pairBeingMerged,
          @SuppressWarnings("unused")  final Object [] moreOptions)
      {
        Pair<Integer,String> oracleAnswer = new Pair<Integer,String>(expected.paths.tracePathPrefixClosed(question),null);
        return oracleAnswer;       
      }
     
    };
    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);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }
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    for(String [] path:plus)
      assert AbstractOracle.USER_ACCEPTED == expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    for(String [] path:minus)
      assert AbstractOracle.USER_ACCEPTED != expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    // Visualiser.getVisualiser()
    Learner l = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,testConfig,null,null))
    {
      @Override
      public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
      {
        // Check that compatibility score computation gives the same response as if we did merge and computed a difference between the number of states.
        ScoreMode origScore = original.config.getLearnerScoreMode();original.config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);
        long compatibilityScore = original.pairscores.computePairCompatibilityScore(pair);
        original.config.setLearnerScoreMode(origScore);
        LearnerGraph outcome = super.MergeAndDeterminize(original, pair);
        Assert.assertEquals(compatibilityScore+1,original.getStateNumber()-outcome.getStateNumber());
        return outcome;
      }

      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
          @SuppressWarnings("unused") List<Boolean> acceptedElements,
          @SuppressWarnings("unused") PairScore pairBeingMerged,
          @SuppressWarnings("unused")  final Object [] moreOptions)
      {
        Pair<Integer,String> oracleAnswer = new Pair<Integer,String>(expected.paths.tracePathPrefixClosed(question),null);
        return oracleAnswer;       
      }
     
    };
    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);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }
View Full Code Here
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    for(String [] path:plus)
      assert AbstractOracle.USER_ACCEPTED == expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    for(String [] path:minus)
      assert AbstractOracle.USER_ACCEPTED != expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    // Visualiser.getVisualiser()
    Learner l = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,testConfig,null,null))
    {
      @Override
      public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
      {
        // Check that compatibility score computation gives the same response as if we did merge and computed a difference between the number of states.
        ScoreMode origScore = original.config.getLearnerScoreMode();original.config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);
        long compatibilityScore = original.pairscores.computePairCompatibilityScore(pair);
        original.config.setLearnerScoreMode(origScore);
        LearnerGraph outcome = super.MergeAndDeterminize(original, pair);
        Assert.assertEquals(compatibilityScore+1,original.getStateNumber()-outcome.getStateNumber());
        return outcome;
      }

      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
          @SuppressWarnings("unused") List<Boolean> acceptedElements,
          @SuppressWarnings("unused") PairScore pairBeingMerged,
          @SuppressWarnings("unused")  final Object [] moreOptions)
      {
        Pair<Integer,String> oracleAnswer = new Pair<Integer,String>(expected.paths.tracePathPrefixClosed(question),null);
        return oracleAnswer;       
      }
     
    };
    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);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    Assert.assertNull(WMethod.checkM(learntMachineNoRejects, expected));
  }
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    public static void main(String[] args) {
        QSMTool tool = new QSMTool();
        tool.loadConfig(args[0]);
        if (tool.showLTL) {
            Learner l = new RPNIUniversalLearner(null, tool.learnerInitConfiguration);
            LTL_to_ba ba = new LTL_to_ba(tool.learnerInitConfiguration.config);
            if (ba.ltlToBA(tool.learnerInitConfiguration.ifthenSequences, l.init(tool.sPlus, tool.sMinus), true,
                    GlobalConfiguration.getConfiguration().getProperty(GlobalConfiguration.G_PROPERTIES.LTL2BA))) {
                try {
                    LearnerGraph graph = Transform.ltlToIfThenAutomaton(ba.getLTLgraph().pathroutines.buildDeterministicGraph());
                    DirectedSparseGraph gr = graph.pathroutines.getGraph();
                    PathRoutines.convertPairAssociationsToTransitions(gr, graph, tool.learnerInitConfiguration.config);
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    for(String [] path:plus)
      assert AbstractOracle.USER_ACCEPTED == expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    for(String [] path:minus)
      assert AbstractOracle.USER_ACCEPTED != expected.paths.tracePathPrefixClosed(AbstractLearnerGraph.buildList(Arrays.asList(path),config,getLabelConverter()));
    // Visualiser.getVisualiser()
    Learner l = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,testConfig,null,null))
    {
      @Override
      public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
      {
        // Check that compatibility score computation gives the same response as if we did merge and computed a difference between the number of states.
        ScoreMode origScore = original.config.getLearnerScoreMode();original.config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);
        long compatibilityScore = original.pairscores.computePairCompatibilityScore(pair);
        original.config.setLearnerScoreMode(origScore);
        LearnerGraph outcome = super.MergeAndDeterminize(original, pair);
        Assert.assertEquals(compatibilityScore+1,original.getStateNumber()-outcome.getStateNumber());
        return outcome;
      }

      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
          @SuppressWarnings("unused") List<Boolean> acceptedElements,
          @SuppressWarnings("unused") PairScore pairBeingMerged,
          @SuppressWarnings("unused")  final Object [] moreOptions)
      {
        Pair<Integer,String> oracleAnswer = new Pair<Integer,String>(expected.paths.tracePathPrefixClosed(question),null);
        return oracleAnswer;       
      }
    };
    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);
    AbstractPathRoutines.removeRejectStates(learntStructureA,learntMachineNoRejects);
    /*
    if (null != WMethod.checkM(learntMachineNoRejects, expected))
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    {
      initial.plus.addAll(evalData.labelDetails.getSPlus());initial.minus.addAll(evalData.labelDetails.getSMinus());
    }
    simulator.setNextElement(nextElement);

    Learner learner2 = null;

    if (logFileName.contains(Configuration.LEARNER.LEARNER_BLUEFRINGE_DEC2007.name()))
    {// have to patch the learner.
     
      learner2 = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,evalData.config,null,null))
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    public static void main(String[] args) {
        QSMTool tool = new QSMTool();
        tool.loadConfig(args[0]);
        if (tool.showLTL) {
            Learner l = new RPNIUniversalLearner(null, tool.learnerInitConfiguration);
            LTL_to_ba ba = new LTL_to_ba(tool.learnerInitConfiguration.config, tool.learnerInitConfiguration.getLabelConverter());
            if (ba.ltlToBA(tool.learnerInitConfiguration.ifthenSequences, l.init(tool.sPlus, tool.sMinus), true,
                    GlobalConfiguration.getConfiguration().getProperty(GlobalConfiguration.G_PROPERTIES.LTL2BA))) {
                try {
                    LearnerGraph graph = Transform.ltlToIfThenAutomaton(ba.getLTLgraph().pathroutines.buildDeterministicGraph());
                    DirectedSparseGraph gr = graph.pathroutines.getGraph();
                    PathRoutines.convertPairAssociationsToTransitions(gr, graph, tool.learnerInitConfiguration.config,tool.learnerInitConfiguration.getLabelConverter());
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      config.setMinCertaintyThreshold(2);
      //config.setAskQuestions(false);
      //config.setKlimit(2);
      //config.setLearnerScoreMode(Configuration.ScoreMode.KTAILS);
      config.setLearnerScoreMode(Configuration.ScoreMode.CONVENTIONAL);
      Learner l = new AccuracyTrackerDecorator(new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
      {
        @Override
        public Pair<Integer,String> CheckWithEndUser(
            @SuppressWarnings("unused")  LearnerGraph model,
            List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
            @SuppressWarnings("unused") List<Boolean> acceptedElements,
            @SuppressWarnings("unused") PairScore pairBeingMerged,
            @SuppressWarnings("unused") final Object [] moreOptions)
        {
          questionNumber.addAndGet(1);
          return new Pair<Integer,String>(graph.paths.tracePathPrefixClosed(question),null);
        }
      }
      , graph);
      sMinus = rpg.getAllSequencesPercentageInterval(1);

      learn(l,sMinus);
      result = result + l.getResult();
    }
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      //config.setKlimit(2);
      //config.setLearnerScoreMode(Configuration.ScoreMode.KTAILS);
      config.setLearnerScoreMode(Configuration.ScoreMode.CONVENTIONAL);
      LearnerEvaluationConfiguration evaluationConfiguration = new LearnerEvaluationConfiguration(null,null,config,null,null);
      evaluationConfiguration.setLabelConverter(experiment.getLabelConverter());
      Learner l = new AccuracyTrackerDecorator(new RPNIUniversalLearner(null,evaluationConfiguration)
      {
        @Override
        public Pair<Integer,String> CheckWithEndUser(
            @SuppressWarnings("unused")  LearnerGraph model,
            List<Label> question, @SuppressWarnings("unused") int valueForNoRestart,
            @SuppressWarnings("unused") List<Boolean> acceptedElements,
            @SuppressWarnings("unused") PairScore pairBeingMerged,
            @SuppressWarnings("unused") final Object [] moreOptions)
        {
          questionNumber.addAndGet(1);
          return new Pair<Integer,String>(graph.paths.tracePathPrefixClosed(question),null);
        }
      }
      , graph);
      sMinus = rpg.getAllSequencesPercentageInterval(1);

      learn(l,sMinus);
      result = result + l.getResult();
    }
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          innerLearner = new RPNIUniversalLearner(PickNegativesVisualiser.this, conf);
        else
              innerLearner = new RPNIUniversalLearner(PickNegativesVisualiser.this, conf);// at this point ifthenSequences will always be null.
       
        innerLearner.addObserver(PickNegativesVisualiser.this);
        Learner mainDecorator = new AutoAnswers(innerLearner);
        if (conf.labelDetails != null)
          mainDecorator = new SmtLearnerDecorator(mainDecorator,conf.labelDetails);
            if (whomToNotify != null) whomToNotify.threadStarted();
            LearnerGraph graph = mainDecorator.learnMachine(sPlus, sMinus);
            if (graph != null)
            {
              DirectedSparseGraph learnt = graph.pathroutines.getGraph();
              if(conf.config.isGenerateTextOutput())
                OutputUtil.generateTextOutput(learnt,"textOutput.txt");
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Related Classes of statechum.analysis.learning.Learner

  • statechum.analysis.learning.ErlangOracleVisualiser
  • statechum.analysis.learning.experiments.IterativeEvaluatorExperiment$IterativeEvaluator
  • statechum.analysis.learning.observers.TestRecorderIntegration
  • statechum.analysis.learning.PickNegativesVisualiser
  • statechum.analysis.learning.Test_CheckLearnerAgainstLog
  • statechum.analysis.learning.TestRpniLearner
  • statechum.apps.QSMTool
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