Package statechum.model.testset

Examples of statechum.model.testset.PTASequenceEngine

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    System.out.println(pr.getPosprecision()+", "+pr.getPosrecall()+", "+pr.getNegprecision()+", "+pr.getNegrecall());
  }
 
  public static PosNegPrecisionRecall compare(LearnerGraph specfsm, LearnerGraph imp){
    PTA_computePrecisionRecall precRec = new PTA_computePrecisionRecall(imp);
    PTASequenceEngine engine = new PTA_FSMStructure(specfsm,null);
    SequenceSet partialPTA = engine.new SequenceSet();partialPTA.setIdentity();
    partialPTA = partialPTA.cross(specfsm.wmethod.getFullTestSet(1));
    precRec.crossWith(engine);return precRec.getPosNegPrecisionRecallNum();
  }
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    precRec.crossWith(engine);return precRec.getPosNegPrecisionRecallNum();
  }
 
  public static PosNegPrecisionRecall compare(Collection<List<String>> tests, LearnerGraph specfsm, LearnerGraph imp){
    PTA_computePrecisionRecall precRec = new PTA_computePrecisionRecall(imp);
    PTASequenceEngine engine = new PTA_FSMStructure(specfsm,null);
    SequenceSet partialPTA = engine.new SequenceSet();partialPTA.setIdentity();
    partialPTA = partialPTA.cross(tests);
    precRec.crossWith(engine);return precRec.getPosNegPrecisionRecallNum();
  }
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    return outcome;
  }
 
  public LearnerGraph learn(PTASequenceEngine engineArg, boolean useNegatives)
  {
    PTASequenceEngine engine = null;
    if (!useNegatives)
    {
      PTASequenceEngine positives = new PTASequenceEngine();positives.init(new Automaton());
      SequenceSet initSeq = positives.new SequenceSet();initSeq.setIdentity();
      initSeq.cross(engineArg.getData());
      engine = positives;
    }
    else
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      {
        theOnlyStateReject = true;break;
      }
    final Boolean rejectAllStates = new Boolean(theOnlyStateReject);
    final AtomicBoolean statesAccept = new AtomicBoolean(true);
    PTASequenceEngine allSequences = new PTASequenceEngine();
    allSequences.init(new PTASequenceSetAutomaton()
    {
      @Override
      public Object getTheOnlyState() {
        return statesAccept.get()?accept:reject;
      }
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    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      PTASequenceEngine engine = buildPTA(plusStrings, minusStrings);
      checkPTAConsistency(engine, plusStrings, true);if (engine.numberOfLeafNodes()>0) checkPTAConsistency(engine, minusStrings, false);
      l.init(engine,0,0);
      actualD = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
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    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      PTASequenceEngine engine = buildPTA(plusStrings, minusStrings);
      checkPTAConsistency(engine, plusStrings, true);if (engine.numberOfLeafNodes()>0) checkPTAConsistency(engine, minusStrings, false);
      l.init(engine,0,0);
      actualD = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
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          @SuppressWarnings("unused") final Object [] moreOptions)
      {
        return new Pair<Integer,String>(graph.paths.tracePath(question),null);
      }
    };
    PTASequenceEngine sPlus = null, sMinus = null;int percent = 10;
    sPlus = rpg.getExtraSequences(percent/10-1);sMinus = rpg.getAllSequences(percent/10-1);

    int ptaSize = sMinus.numberOfLeafNodes();
    l.init(sMinus, ptaSize,ptaSize);// our imaginary positives are prefixes of negatives.
    DirectedSparseGraph learningOutcome = l.learnMachine();
    l.setQuestionCounter(0);
    LearnerGraph learned = new LearnerGraph(learningOutcome,config);
    PTA_computePrecisionRecall precRec = new PTA_computePrecisionRecall(learned);
    PTASequenceEngine engine = new PTA_FSMStructure(graph);
    precRec.crossWith(sMinus);
    SequenceSet ptaTestSet = engine.new SequenceSet();ptaTestSet.setIdentity();
    ptaTestSet = ptaTestSet.cross(graph.wmethod.getFullTestSet(1));
    PosNegPrecisionRecall prNeg = precRec.crossWith(engine);
    System.out.println("pre: "+prNeg.getPrecision()+" rec: "+prNeg.getRecall());
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   /** Records the initial PTA. The Pta from the paper is recorded as the initial automaton and the automaton passed as an argument is recorded as the expected outcome of learning of the evaluation configuration.
    */
   public void recordInitialConfiguration(LearnerGraph smallPTA) throws IOException
   {
       PTASequenceEngine engine = collectionOfTraces.get(UAVAllSeeds).tracesForUAVandFrame.get(UAVAllSeeds).get(maxFrameNumber);
      
       FileOutputStream log = new java.io.FileOutputStream("resources/largePTA/VeryLargePTA.zip");
       RPNIBlueFringeVariability ourLearner = new RPNIBlueFringeVariability(learnerInitConfiguration,true,null,null);
       LearnerGraph automatonAfterInit = ourLearner.getLearner().init(engine,0,0);
       final Configuration shallowCopy = automatonAfterInit.config.copy();shallowCopy.setLearnerCloneGraph(false);
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      {
        theOnlyStateReject = true;break;
      }
    final Boolean rejectAllStates = new Boolean(theOnlyStateReject);
    final AtomicBoolean statesAccept = new AtomicBoolean(true);
    PTASequenceEngine allSequences = new PTASequenceEngine();
   
    // Here we are building a PTA which consists of accept states for plusStrings and reject-states
    // for minusStrings. This way if a plus string is a prefix of a plusString, the prefix will
    // be labelled with Boolean(true) states and the suffix - with Boolean(false) states.
    // Note that the suffix may contain many states.
    allSequences.init(new PTASequenceSetAutomaton()
    {
      @Override
      public Object getTheOnlyState() {
        return statesAccept.get()?accept:reject;
      }
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    try
    {
      Configuration config = mainConfiguration.copy();
      RPNIUniversalLearner l = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null));
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      PTASequenceEngine engine = buildPTA(plusStrings, minusStrings);
      checkPTAConsistency(engine, plusStrings, true);if (engine.numberOfLeafNodes()>0) checkPTAConsistency(engine, minusStrings, false);
      l.init(engine,0,0);
      actualD = l.getTentativeAutomaton().pathroutines.getGraph();
    }
    catch(IllegalArgumentException e)
    {
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Related Classes of statechum.model.testset.PTASequenceEngine

  • statechum.analysis.learning.ErlangOracleLearner
  • statechum.analysis.learning.experiments.Benchmarklinear
  • statechum.analysis.learning.experiments.CompareGraphs
  • statechum.analysis.learning.experiments.ManualAccuracyTracker
  • statechum.analysis.learning.experiments.PaperUAS
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringe
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringeTestVariability
  • statechum.analysis.learning.experiments.PaperUAS$RPNIVariabilityExperiment
  • statechum.analysis.learning.experiments.StructuralDifferencesPaper
  • statechum.analysis.learning.linear.GDLearnerGraph$DDRH_BCR
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