Package statechum.analysis.learning.rpnicore

Examples of statechum.analysis.learning.rpnicore.LearnerGraph.clearColours()

260
261
262
263
264
265
266
267
268
269
270
          assert scoreInitialMerge >= 0;
          ptaToUseForInference = MergeStates.mergeCollectionOfVertices(pta, null, verticesToMergeInitialMerge);
          final CmpVertex vertexWithMostTransitions = MarkovPassivePairSelection.findVertexWithMostTransitions(ptaToUseForInference,MarkovClassifier.computeInverseGraph(pta));
          if (useMostConnectedVertexToStartLearning)
          {
            ptaToUseForInference.clearColours();ptaToUseForInference.getInit().setColour(null);vertexWithMostTransitions.setColour(JUConstants.RED);
          }
          LearnerGraphND inverseOfPtaAfterInitialMerge = MarkovClassifier.computeInverseGraph(ptaToUseForInference);
          System.out.println("Centre vertex: "+vertexWithMostTransitions+" number of transitions: "+MarkovPassivePairSelection.countTransitions(ptaToUseForInference, inverseOfPtaAfterInitialMerge, vertexWithMostTransitions));
        }
View Full Code Here
645
646
647
648
649
650
651
652
653
654
655
      LearnerGraph pta=new LearnerGraph(config);
      for(List<Label> seq:sPlus)
        pta.paths.augmentPTA(seq,true,false,null);
      for(List<Label> seq:sMinus)
        pta.paths.augmentPTA(seq,false,false,null);
      pta.clearColours();
      new MarkovClassifier(m, pta).updateMarkov(false);// construct Markov chain
      // For Markov, we do not need to learn anything at all - our Markov matrix contains enough information to classify paths and hence compare it to the reference graph.
      ConfusionMatrix mat = DiffExperiments.classifyAgainstMarkov(testSet, referenceGraph, m);
      DifferenceToReferenceLanguageBCR differenceBCRMarkov = new DifferenceToReferenceLanguageBCR(mat);
     
View Full Code Here
918
919
920
921
922
923
924
925
926
927
928
                            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)
                              {
View Full Code Here
994
995
996
997
998
999
1000
1001
1002
1003
1004
                            LearnerGraph pta=new LearnerGraph(learnerInitConfiguration.config);
                            for(List<Label> seq:sPlus)
                              pta.paths.augmentPTA(seq,true,false,null);
                            for(List<Label> seq:sMinus)
                              pta.paths.augmentPTA(seq,false,false,null);
                            pta.clearColours();
                            ReferenceLearner learner = new ReferenceLearner(learnerInitConfiguration,null,pta,false) {

                              @Override
                              public Stack<PairScore> ChooseStatePairs(LearnerGraph graph)
                              {
View Full Code Here
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
                            LearnerGraph pta=new LearnerGraph(learnerInitConfiguration.config);
                            for(List<Label> seq:sPlus)
                              pta.paths.augmentPTA(seq,true,false,null);
                            for(List<Label> seq:sMinus)
                              pta.paths.augmentPTA(seq,false,false,null);
                            pta.clearColours();
                            KTailsReferenceLearner learner = new KTailsReferenceLearner(learnerInitConfiguration,pta,true,1) {

                              @Override
                              public Stack<PairScore> ChooseStatePairs(LearnerGraph graph)
                              {
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. 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.