Package statechum.analysis.learning.MarkovClassifier

Examples of statechum.analysis.learning.MarkovClassifier.ConsistencyChecker

216
217
218
219
220
221
222
223
224
225
226
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

        LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
       
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklisting();
        long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(referenceGraph, m, checker, false);
       
        learnerOfPairs = new LearnerMarkovPassive(learnerEval,referenceGraph,pta);learnerOfPairs.setMarkovModel(m);

        learnerOfPairs.setScoreComputationOverride(new RedPriorityOverBluePairSelectionRoutine(m));
View Full Code Here
216
217
218
219
220
221
222
223
224
225
226
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

        LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
       
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklisting();
        long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(referenceGraph, m, checker, false);
       
        learnerOfPairs = new LearnerMarkovPassive(learnerEval,referenceGraph,pta);learnerOfPairs.setMarkovModel(m);

        learnerOfPairs.setScoreComputationOverride(new RedPriorityOverBluePairSelectionRoutine(m));
View Full Code Here
225
226
227
228
229
230
231
232
233
234
235
       
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

        LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
        long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);

        LearnerGraph ptaToUseForInference = pta;
        Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
               
View Full Code Here
83
84
85
86
87
88
89
90
91
92
93
    LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
    int genScore = ptaClassifier.graph.pairscores.computePairCompatibilityScore_general(null, constructPairsToMergeBasedOnSetsToMerge(ptaClassifier.graph.transitionMatrix.keySet(),verticesToMergeBasedOnInitialPTA), verticesToMerge);
    LearnerGraph graph = MergeStates.mergeCollectionOfVertices(ptaClassifier.graph, null, verticesToMerge);
   
    Set<CmpVertex> tr=graph.transform.trimGraph(10, graph.getInit()).transitionMatrix.keySet();
    ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistency();

    constructPairsToMergeBasedOnSetsToMerge(graph.transitionMatrix.keySet(),verticesToMergeBasedOnInitialPTA);   
    for(CmpVertex v0:tr)
      for(CmpVertex v1:tr)
        if (v0 != v1)
View Full Code Here
320
321
322
323
324
325
326
327
328
329
330
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

        // now use pathsToMerge to compute which states can/cannot be merged together.
        LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklisting();
        //long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);
        //System.out.println("Inconsistency of trimmed reference : "+inconsistencyForTheReferenceGraph);
       
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
View Full Code Here
869
870
871
872
873
874
875
876
877
878
879
    // transitions in the vicinity of the added ones. For instance, where a path has been folded in with some transitions sticking out, those new ones
    // may be inconsistent with predictions, based on the transitions in the red part of the graph.

    // mapping map to store all paths leave each state in different length
    double tentativeScore=0;
    ConsistencyChecker checker = new MarkovClassifier.InconsistencyNullVsPredicted();
    MarkovClassifier resultClassifier = new MarkovClassifier(cl.model,result);
    for(Entry<CmpVertex,Collection<Label>> entry:labelsAdded.entrySet())
      if (!entry.getValue().isEmpty())
      {
        double numberOfInconsistencies = resultClassifier.checkFanoutInconsistency(entry.getKey(),checker,false);
View Full Code Here
197
198
199
200
201
202
203
204
205
206
207
      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);

      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.
View Full Code Here
733
734
735
736
737
738
739
740
741
742
743
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

        // now use pathsToMerge to compute which states can/cannot be merged together.
        LearnerGraph trimmedReference = trimUncoveredTransitions(pta,referenceGraph);
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklisting();
        //long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);
        //System.out.println("Inconsistency of trimmed reference : "+inconsistencyForTheReferenceGraph);
       
        //if (inconsistencyForTheReferenceGraph != 53)
        //  break;// ignore automata where we get good results.
View Full Code Here
234
235
236
237
238
239
240
241
242
243
244
     
      final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
      LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

      LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
      final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
      long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);

      LearnerGraph ptaToUseForInference = pta;
      Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
             
View Full Code Here
227
228
229
230
231
232
233
234
235
236
237
       
        final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
        LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);

//        LearnerGraph trimmedReference = MarkovPassivePairSelection.trimUncoveredTransitions(pta,referenceGraph);
        final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
//        long inconsistencyForTheReferenceGraph = MarkovClassifier.computeInconsistency(trimmedReference, m, checker,false);

        LearnerGraph ptaToUseForInference = pta;
        Collection<Set<CmpVertex>> verticesToMergeBasedOnInitialPTA=null;
               
View Full Code Here
TOP

Related Classes of statechum.analysis.learning.MarkovClassifier.ConsistencyChecker

  • statechum.analysis.Erlang.Synapse$StatechumProcess
  • statechum.analysis.learning.experiments.PairSelection.ASE2014$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.Cav2014$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.CVS$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.CVS_With_Random_traces_Generation$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.MarkovLearnerExperimentWithStatisticalAnalysis$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.MarkovPassivePairSelection
  • statechum.analysis.learning.experiments.PairSelection.MarkovPassivePairSelection$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.MarkovScoreComputation
  • statechum.analysis.learning.experiments.PairSelection.TestLearnFromTracesUsingMarkov
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.