List of buildVerticesToMergeForPaths() Examples

Examples of buildVerticesToMergeForPaths()

statechum.analysis.learning.MarkovClassifier.buildVerticesToMergeForPaths()
Given a collection of paths, makes it possible to merge states from which the provided paths can be followed. Where multiple paths can be followed from the same state, merges all states from which any of the paths can be followed. Depending on the input, can be used for paths in the forward direction or in the inverse one (in which case graphsToCheckForPaths should also be an inverse of a graph of interest, to match the paths being considered). @param paths collection of sequences of labels, we will merge all states that have the same sequence leading from them. @return a number of collections of vertices to merge. Every two collections are non-intersecting but may not cover all states in the original graph.

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

        //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.buildVerticesToMergeForPaths()

    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }
  
  public static LearnerGraph checkIfSingleStateLoopsCanBeFormed(MarkovClassifier ptaClassifier,LearnerGraph referenceGraph,final Collection<List<Label>> pathsOfInterest)
  {

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

    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }
  
  public static LearnerGraph checkIfSingleStateLoopsCanBeFormed(MarkovClassifier ptaClassifier,LearnerGraph referenceGraph,final Collection<List<Label>> pathsOfInterest)
  {

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

        //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.buildVerticesToMergeForPaths()

      {
        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);
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
        int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
        assert scoreInitialMerge >= 0;

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

        {
          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);
          LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
          int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
          assert scoreInitialMerge >= 0;

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

        //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);
        assert scoreInitialMerge >= 0;

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

    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }
  
  public static LearnerGraph checkIfSingleStateLoopsCanBeFormed(MarkovClassifier ptaClassifier,LearnerGraph referenceGraph,final Collection<List<Label>> pathsOfInterest)
  {

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

        {
          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);
          LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
          int scoreInitialMerge = pta.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
          assert scoreInitialMerge >= 0;

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

                              {
                                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);
                                LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMergeInitialMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
                                int scoreInitialMerge = ptaInitial.pairscores.computePairCompatibilityScore_general(null, pairsListInitialMerge, verticesToMergeInitialMerge);
                                assert scoreInitialMerge >= 0;

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