List of clearColours() Examples

Examples of clearColours()

statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()
Resets all the colour labelling to the initial value.

Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

        PaperUAS.computePTASize(selectionID+" with unique "+uniqueFromInitial+" : ", pta, referenceGraph);
      }*/
      //Visualiser.updateFrame(referenceGraph, pta);
      //Visualiser.waitForKey();
      
      pta.clearColours();
      
      if (!onlyUsePositives)
      {
        assert pta.getStateNumber() > pta.getAcceptStateNumber() : "graph with only accept states but onlyUsePositives is not set";
        Map<Label,Set<Label>> infeasiblePairs = PairQualityLearner.computeInfeasiblePairs(referenceGraph);

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

                            final MarkovModel m= new MarkovModel(3,true,true);


                            new MarkovClassifier(m, pta).updateMarkov(false);// construct Markov chain if asked for.
                            final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
                          
                            pta.clearColours();
                            EDSM_MarkovLearner learner = new EDSM_MarkovLearner(learnerInitConfiguration,pta,0) {


                              @Override
                              public Stack<PairScore> ChooseStatePairs(LearnerGraph graph) 
                              {

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

          pta.paths.augmentPTA(generator.getAllSequences(0));// the PTA will have very few reject-states because we are generating few sequences and hence there will be few negative sequences.
          // In order to approximate the behaviour of our case study, we need to compute which pairs are not allowed from a reference graph and use those as if-then automata to start the inference.
          
        //pta.paths.augmentPTA(referenceGraph.wmethod.computeNewTestSet(referenceGraph.getInit(),1));
        
        pta.clearColours();
        synchronized (AbstractLearnerGraph.syncObj) {
          //PaperUAS.computePTASize(selectionID+" attempt: "+attempt+" with unique: ", pta, referenceGraph);
        }
        
        if (!onlyUsePositives)

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovUniversalLearner m= new MarkovUniversalLearner(chunkLen);
        m.createMarkovLearner(sPlus, sMinus);
        
        pta.clearColours();
        synchronized (AbstractLearnerGraph.syncObj) {
          //PaperUAS.computePTASize(selectionID+" attempt: "+attempt+" with unique: ", pta, referenceGraph);
        }
        
        if (!onlyUsePositives)

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

    */
    //DifferentFSMException diff = WMethod.checkM(ptaExpected, ptaExpected.getInit(), pta, pta.getInit(), WMethod.VERTEX_COMPARISON_KIND.NONE);Assert.assertNull(diff);
    final MarkovModel m= new MarkovModel(chunkLen,true,true,true);


    new MarkovClassifier(m, pta).updateMarkov(false);
    pta.clearColours();


    assert pta.getStateNumber() == pta.getAcceptStateNumber() : "graph with negatives but onlyUsePositives is set";
      
    final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
    LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

    LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
    int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
    assert scoreInitialMerge >= 0;
    final LearnerGraph ptaAfterInitialMerge = MergeStates.mergeCollectionOfVertices(pta, null, verticesToMergeInitialMerge);
    final CmpVertex vertexWithMostTransitions = MarkovPassivePairSelection.findVertexWithMostTransitions(ptaAfterInitialMerge,MarkovClassifier.computeInverseGraph(pta));
    ptaAfterInitialMerge.clearColours();ptaAfterInitialMerge.getInit().setColour(null);vertexWithMostTransitions.setColour(JUConstants.RED);
    ptaClassifier = new MarkovClassifier(m,ptaAfterInitialMerge);// rebuild the classifier
    LearnerGraphND inverseOfPtaAfterInitialMerge = MarkovClassifier.computeInverseGraph(ptaAfterInitialMerge);
    //System.out.println("Centre vertex: "+vertexWithMostTransitions+" "+MarkovPassivePairSelection.countTransitions(ptaAfterInitialMerge, inverseOfPtaAfterInitialMerge, vertexWithMostTransitions));
    
    // These have been recorded, there is no specific reason for the numbers to be as they are. We need them for regression testing.

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

    
        final MarkovModel m= new MarkovModel(chunkLen,true,true);


        new MarkovClassifier(m, pta).updateMarkov(false);// construct Markov chain if asked for.
        
        pta.clearColours();


        if (!onlyUsePositives)
          assert pta.getStateNumber() > pta.getAcceptStateNumber() : "graph with only accept states but onlyUsePositives is not set";
        else 
          assert pta.getStateNumber() == pta.getAcceptStateNumber() : "graph with negatives but onlyUsePositives is set";

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

          assert scoreInitialMerge >= 0;
          ptaToUseForInference = MergeStates.mergeCollectionOfVertices(pta, null, verticesToMergeInitialMerge);
          final CmpVertex vertexWithMostTransitions = MarkovPassivePairSelection.findVertexWithMostTransitions(ptaToUseForInference,MarkovClassifier.computeInverseGraph(pta));
          if (useMostConnectedVertexToStartLearning)
          {
            ptaToUseForInference.clearColours();ptaToUseForInference.getInit().setColour(null);vertexWithMostTransitions.setColour(JUConstants.RED);
          }
          LearnerGraphND inverseOfPtaAfterInitialMerge = MarkovClassifier.computeInverseGraph(ptaToUseForInference);
          System.out.println("Centre vertex: "+vertexWithMostTransitions+" number of transitions: "+MarkovPassivePairSelection.countTransitions(ptaToUseForInference, inverseOfPtaAfterInitialMerge, vertexWithMostTransitions));
        }

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        pta.clearColours();
        synchronized (AbstractLearnerGraph.syncObj) {
          //PaperUAS.computePTASize(selectionID+" attempt: "+attempt+" with unique: ", pta, referenceGraph);
        }
        
        if (!onlyUsePositives)

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Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
        int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
        assert scoreInitialMerge >= 0;
        final LearnerGraph ptaAfterInitialMerge = MergeStates.mergeCollectionOfVertices(pta, null, verticesToMergeInitialMerge);
        final CmpVertex vertexWithMostTransitions = MarkovPassivePairSelection.findVertexWithMostTransitions(ptaAfterInitialMerge,MarkovClassifier.computeInverseGraph(pta));
        ptaAfterInitialMerge.clearColours();ptaAfterInitialMerge.getInit().setColour(null);vertexWithMostTransitions.setColour(JUConstants.RED);
        ptaClassifier = new MarkovClassifier(m,ptaAfterInitialMerge);// rebuild the classifier
        LearnerGraphND inverseOfPtaAfterInitialMerge = MarkovClassifier.computeInverseGraph(ptaAfterInitialMerge);
        System.out.println("Centre vertex: "+vertexWithMostTransitions+" "+MarkovPassivePairSelection.countTransitions(ptaAfterInitialMerge, inverseOfPtaAfterInitialMerge, vertexWithMostTransitions));
        
        //learnerEval.config.setGeneralisationThreshold(1);

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