Package statechum.model.testset.PTASequenceEngine

Examples of statechum.model.testset.PTASequenceEngine.FilterPredicate


      final PTASequenceEngine currentPTA = generator.getAllSequences(i);
      Collection<List<Label>> currentSequences = currentPTA.getData(PTASequenceEngine.truePred);
      Assert.assertTrue(2*posOrNegPerChunk*(i+1)> currentSequences.size());
      int positive = 0,negative=0;
      PTASequenceEngine positivePTA = currentPTA.filter(currentPTA.getFSM_filterPredicate());
      PTASequenceEngine negativePTA = currentPTA.filter(new FilterPredicate() {
        FilterPredicate origFilter = currentPTA.getFSM_filterPredicate();
       
        @Override
        public boolean shouldBeReturned(Object name) {
          return !origFilter.shouldBeReturned(name);
View Full Code Here


  /** Two-thread computation of precision/recall by going through all the sequences. */
  private static final PosNegPrecisionRecall computePrecisionRecall(final LearnerGraph graph, final PTASequenceEngine sequences)
  {
    final Collection<List<Label>> positiveRel = sequences.filter(sequences.getFSM_filterPredicate()).getData(),
      negativeRel = sequences.getData(new FilterPredicate() {
        FilterPredicate origFilter = sequences.getFSM_filterPredicate();
       
        @Override
        public boolean shouldBeReturned(Object name) {
          return !origFilter.shouldBeReturned(name);
View Full Code Here

        });


      if (onlyUsePositives)
      {
        pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        }));
View Full Code Here

          });


        if (onlyUsePositives)
        {
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
View Full Code Here

              return len;
            }
          });

        if (onlyUsePositives)
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
        else
          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));
   
        List<List<Label>> sPlus = generator.getAllSequences(0).getData(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
        List<List<Label>> sMinus= generator.getAllSequences(0).getData(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return !((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
View Full Code Here

          pta.paths.augmentPTA(seq, referenceGraph.getVertex(seq) != null, false, null);
        }*/
        //pta.paths.augmentPTA(referenceGraph.wmethod.computeNewTestSet(referenceGraph.getInit(),1));// this one will not set any states as rejects because it uses shouldbereturned
        //referenceGraph.pathroutines.completeGraph(referenceGraph.nextID(false));
        if (onlyUsePositives)
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
View Full Code Here

              return len;
            }
          });

        if (onlyUsePositives)
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
        else
          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));
   
        List<List<Label>> sPlus = generator.getAllSequences(0).getData(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
        List<List<Label>> sMinus= generator.getAllSequences(0).getData(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return !((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
View Full Code Here

          pta.paths.augmentPTA(seq, referenceGraph.getVertex(seq) != null, false, null);
        }*/
        //pta.paths.augmentPTA(referenceGraph.wmethod.computeNewTestSet(referenceGraph.getInit(),1));// this one will not set any states as rejects because it uses shouldbereturned
        //referenceGraph.pathroutines.completeGraph(referenceGraph.nextID(false));
        if (onlyUsePositives)
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
View Full Code Here

  /** Two-thread computation of precision/recall by going through all the sequences. */
  private static final PosNegPrecisionRecall computePrecisionRecall(final LearnerGraph graph, final PTASequenceEngine sequences)
  {
    final Collection<List<String>> positiveRel = sequences.filter(sequences.getFSM_filterPredicate()).getData(),
      negativeRel = sequences.getData(new FilterPredicate() {
        FilterPredicate origFilter = sequences.getFSM_filterPredicate();
       
        @Override
        public boolean shouldBeReturned(Object name) {
          return !origFilter.shouldBeReturned(name);
View Full Code Here

          });


        if (onlyUsePositives)
        {
          pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
            @Override
            public boolean shouldBeReturned(Object name) {
              return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
            }
          }));
View Full Code Here

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