Package statechum.model.testset

Examples of statechum.model.testset.PTASequenceSet

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    //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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          Collection<List<String>> questionsOrigA = ComputeQuestions.computeQS_orig(pair, scoreComputer,tempOrig);
          //CmpVertex Rnew = tempNew.getVertex(scoreComputer.wmethod.computeShortPathsToAllStates().get(pair.getR()));
          CmpVertex Rnew = tempNew.getStateLearnt();
          assert Rnew == tempNew.getVertex(scoreComputer.wmethod.computeShortPathsToAllStates().get(pair.getR()));
          Collection<List<String>> questionsOrigB = ComputeQuestions.computeQS_orig(new StatePair(Rnew,Rnew), scoreComputer,tempNew);
          PTASequenceSet newQuestions =new PTASequenceSet();newQuestions.addAll(questions);
          assert newQuestions.containsAll(questionsOrigA);
          assert newQuestions.containsAll(questionsOrigB);
        }
       
        if (questions.isEmpty())
          ++counterEmptyQuestions;
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    //l.setGeneralisationThreshold(1);
    //l.setCertaintyThreshold(5);
    testConfig.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);
    LearnerGraph learntStructureA = new LearnerGraph(l.learnMachine(buildSet(plus), buildSet(minus)),expected.config);
    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus));
    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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      Collection<List<String>> questionsOrigA = ComputeQuestions.computeQS_orig(pair, scoreComputer,MergeStates.mergeAndDeterminize(scoreComputer, pair));
      CmpVertex Rnew = tempNew.getStateLearnt();
      assert Rnew == tempNew.getVertex(scoreComputer.wmethod.computeShortPathsToAllStates().get(pair.getR()));
      Collection<List<String>> questionsOrigB = ComputeQuestions.computeQS_orig(new StatePair(Rnew,Rnew), scoreComputer,tempNew);
      PTASequenceSet newQuestions =new PTASequenceSet();newQuestions.addAll(questions);
      assert newQuestions.containsAll(questionsOrigA);
      assert newQuestions.containsAll(questionsOrigB);
    }
   
    return questions;
  }
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    l.init(buildSet(plus), buildSet(minus));
    DirectedSparseGraph learningOutcomeA = l.learnMachine();
    LearnerGraph learntStructureA = new LearnerGraph(learningOutcomeA,testConfig);

    // Now do the same with ptasets instead of real sets
    PTASequenceSet plusPTA = new PTASequenceSet();plusPTA.addAll(buildSet(plus));PTASequenceSet minusPTA = new PTASequenceSet();minusPTA.addAll(buildSet(minus));
    l.init(plusPTA, minusPTA);
    DirectedSparseGraph learningOutcomeB = l.learnMachine();
    LearnerGraph learntStructureB = new LearnerGraph(learningOutcomeB,testConfig);
    WMethod.checkM(learntStructureA, learntStructureB);
    //TestFSMAlgo.checkM(learntStructure,completedGraph,learntStructure.init,expected.init);
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    //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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   * generating walks.
   */
  protected void initAllSequences()
  {
    tag = new StateName(0,false);
    allSequences = new PTASequenceSet(new PercentLabelledPTA());extraSequences = new PTASequenceSet(new PercentLabelledPTA());
  }
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    //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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    //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);
    /*
 
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   * generating walks.
   */
  protected void initAllSequences()
  {
    tag = new StateName(0,false);
    allSequences = new PTASequenceSet(new PercentLabelledPTA());extraSequences = new PTASequenceSet(new PercentLabelledPTA());
  }
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Related Classes of statechum.model.testset.PTASequenceSet

  • statechum.analysis.learning.RPNIBlueAmberFringeLearner
  • statechum.analysis.learning.RPNIBlueFringeLearnerTestComponentOpt
  • statechum.analysis.learning.rpnicore.RandomPathGenerator
  • statechum.analysis.learning.Test_Orig_RPNIBlueFringeLearner
  • statechum.analysis.learning.TestRpniLearner
  • statechum.model.testset.TestPrefixRemovingCollection
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