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()

        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()

        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        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 sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        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()

          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()

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


                            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)

View Full Code Here

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)

View Full Code Here

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)

View Full Code Here

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,false);
        
        pta.clearColours();
        synchronized (AbstractLearnerGraph.syncObj) {
          //PaperUAS.computePTASize(selectionID+" attempt: "+attempt+" with unique: ", pta, referenceGraph);
        }
        
        if (!onlyUsePositives)

View Full Code Here

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)

View Full Code Here

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