List of identifyPathsToMerge() Examples

Examples of identifyPathsToMerge()

statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()
Uses a supplied consistency checker to find paths that uniquely identify states. The supplied consistency checker is used to verify consistency after states deemed identical are merged. @param checker Consistency checker to use for predictions, usually based on a static method from {@link MarkovOutcome}. @return paths to uniquely identify states.

Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
          
        MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
        final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
        final Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);


        /*
        List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
          
        MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
        final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
        final Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);


        /*
        List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

      Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
              
      if (useCentreVertex)
      {
        final MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
        final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
        // These vertices are merged first and then the learning start from the root as normal.
        // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
        verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);
        
        List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()


    final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
    //long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);


    MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
    final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
    // These vertices are merged first and then the learning start from the root as normal.
    // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
    //final Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
                
        if (useCentreVertex)
        {
          final MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
          final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
          // These vertices are merged first and then the learning start from the root as normal.
          // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
          verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);
          
          List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
          
        MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
        final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
        final Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);


        List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
        int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
                
        if (useCentreVertex)
        {
          final MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
          final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
          // These vertices are merged first and then the learning start from the root as normal.
          // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
          verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);
          
          List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

                              final MarkovClassifier ptaClassifier = new MarkovClassifier(m, ptaInitial);ptaClassifier.updateMarkov(false);
                              LearnerGraph ptaToUseForInference = ptaInitial;
                              final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
                              {
                                Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
                                final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
                                // These vertices are merged first and then the learning start from the root as normal.
                                // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
                                verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);
                                
                                List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
                
        if (useCentreVertex)
        {
          final MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
          final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
          // These vertices are merged first and then the learning start from the root as normal.
          // The reason to learn from the root is a memory cost. if we learn from the middle, we can get a better results
          verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);
          
          List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);

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Examples of statechum.analysis.learning.MarkovClassifier.identifyPathsToMerge()

        
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
          
        MarkovClassifier ptaClassifier = new MarkovClassifier(m,pta);
        final List<List<Label>> pathsToMerge=ptaClassifier.identifyPathsToMerge(checker);
        final Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=ptaClassifier.buildVerticesToMergeForPaths(pathsToMerge);


        List<StatePair> pairsListInitialMerge = ptaClassifier.buildVerticesToMergeForPath(pathsToMerge);
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
        int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);

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