Package statechum.analysis.learning.rpnicore

Examples of statechum.analysis.learning.rpnicore.RandomPathGenerator

337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
      outcome.setValue(DOUBLE_V.ACCURACY_W, wSeq.firstElem);
    }
   
    {
      Collection<List<Label>> sequences =new LinkedHashSet<List<Label>>();
      RandomPathGenerator rpg = new RandomPathGenerator(from, new Random(0),4, from.getInit());// the seed for Random should be the same for each file
      long startTime = System.nanoTime();
      rpg.generatePosNeg((graphComplexity+1)*states , 1);
      outcome.setValue(LONG_V.DURATION_RAND,System.nanoTime()-startTime);
      sequences.addAll(rpg.getAllSequences(0).getData(PTASequenceEngine.truePred));
      sequences.addAll(rpg.getExtraSequences(0).getData(PTASequenceEngine.truePred));
      Pair<Double,Long> randSeq = compareLang(from, to, sequences);
      outcome.setValue(LONG_V.DURATION_RAND, outcome.getValue(LONG_V.DURATION_RAND)+randSeq.secondElem);
      outcome.setValue(DOUBLE_V.ACCURACY_RAND, randSeq.firstElem);
    }
   
View Full Code Here
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // test sequences will be distributed around
        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.
        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,
        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.

        final int tracesToGenerate = makeEven(traceQuantity);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                   
            @Override
            public int getLength() {
              return 2*states*alphabet;
            }
   
            @Override
            public int getPrefixLength(int len) {
              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
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
    LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
    //final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,PairQualityLearner.makeEven(states*alphabet*traceMultiplier));

    final int attempt=0;
    LearnerGraph pta = new LearnerGraph(config);
    RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
    final int tracesToGenerate = PairQualityLearner.makeEven(traceQuantity);
    generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                 
        @Override
        public int getLength() {
          return (int)(traceLengthMultiplier*states*alphabet);

        }

        @Override
        public int getPrefixLength(int len) {
          return len;
        }
      });

        pta.paths.augmentPTA(generator.getAllSequences(0).filter(new FilterPredicate() {
          @Override
          public boolean shouldBeReturned(Object name) {
            return ((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        }));
 
      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;
        }
      });
View Full Code Here
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // test sequences will be distributed around
        final int pathLength = generator.getPathLength();
        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.
        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,
        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.
        final int tracesToGenerate = makeEven(traceQuantity);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                   
            @Override
            public int getLength() {
              return 2*states*alphabet;//(rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }
   
            @Override
            public int getPrefixLength(int len) {
              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
899
900
901
902
903
904
905
906
907
908
909
910
911
        final LearnerGraph graphReference = new LearnerGraph(learnerInitConfiguration.config);AbstractPersistence.loadGraph("resources/uas_reference_automaton.xml",graphReference,labelConverter);
       final Collection<List<Label>> wMethod = graphReference.wmethod.getFullTestSet(1);
       int wPos=0;
       for(List<Label> seq:wMethod) if (graphReference.paths.tracePathPrefixClosed(seq) == AbstractOracle.USER_ACCEPTED) wPos++;
       System.out.println("before rnd: "+wMethod.size()+" sequences, "+wPos+" positives");
       RandomPathGenerator pathGen = new RandomPathGenerator(graphReference,new Random(0),5,graphReference.getInit());
       pathGen.generatePosNeg(2*(wMethod.size()-wPos), 1);
       wMethod.addAll(pathGen.getExtraSequences(0).getData());
       System.out.println("after rnd: "+wMethod.size()+" sequences");
      
      
       DrawGraphs gr = new DrawGraphs();
    final RBoxPlot<Integer>
View Full Code Here
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));
     
     
      // try learning the same machine using a random generator selector passed as a parameter.
      LearnerGraph pta = new LearnerGraph(config);
      RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(trainingSample),5,null);
      final int tracesToGenerate = makeEven(traceQuantity);
      generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                 
          @Override
          public int getLength() {
            return (int)(traceLengthMultiplier*states*tracesAlphabet);
          }
 
          @Override
          public int getPrefixLength(int len) {
            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));
 
      final MarkovModel m= new MarkovModel(chunkLen,true,true,false);

      new MarkovClassifier(m, pta).updateMarkov(false);// construct Markov chain if asked for.
     
View Full Code Here
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // test sequences will be distributed around
        final int pathLength = generator.getPathLength();
        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.
        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,
        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.
        final int tracesToGenerate = makeEven(traceQuantity);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                   
            @Override
            public int getLength() {
              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }
   
            @Override
            public int getPrefixLength(int len) {
              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
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
      final Collection<List<Label>> testSet = null;//PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // test sequences will be distributed around
        final int pathLength = generator.getPathLength();
        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.
        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,
        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.
        final int tracesToGenerate = makeEven(states*traceQuantity);
        final Random rnd = new Random(seed*31+attempt);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                   
            @Override
            public int getLength() {
              return (rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }
   
            @Override
            public int getPrefixLength(int len) {
              return len;
            }
          });
        /*
        for(List<Label> seq:referenceGraph.wmethod.computeNewTestSet(1))
        {
          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;
            }
          }));
        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));
       
        pta.clearColours();
View Full Code Here
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
      {// try learning the same machine a few times
        LearnerGraph pta = new LearnerGraph(config);
        RandomPathGenerator generator = new RandomPathGenerator(referenceGraph,new Random(attempt),5,null);
        // The total number of elements in test sequences (alphabet*states*traceQuantity) will be distributed around (random(pathLength)+1). The total size of PTA is a product of these two.
        // For the purpose of generating long traces, we construct as many traces as there are states but these traces have to be rather long,
        // that is, length of traces will be (random(pathLength)+1)*sequencesPerChunk/states and the number of traces generated will be the same as the number of states.
        final int tracesToGenerate = makeEven(traceQuantity);
        generator.generateRandomPosNeg(tracesToGenerate, 1, false, new RandomLengthGenerator() {
                   
            @Override
            public int getLength() {
              return 2*states*alphabet;//(rnd.nextInt(pathLength)+1)*lengthMultiplier;
            }
   
            @Override
            public int getPrefixLength(int len) {
              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
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
      outcome.setValue(DOUBLE_V.ACCURACY_W, wSeq.firstElem);
    }
   
    {
      Collection<List<Label>> sequences =new LinkedHashSet<List<Label>>();
      RandomPathGenerator rpg = new RandomPathGenerator(from, new Random(0),4, from.getInit());// the seed for Random should be the same for each file
      long startTime = System.nanoTime();
      rpg.generatePosNeg((graphComplexity+1)*states , 1);
      outcome.setValue(LONG_V.DURATION_RAND,System.nanoTime()-startTime);
      sequences.addAll(rpg.getAllSequences(0).getData(PTASequenceEngine.truePred));
      sequences.addAll(rpg.getExtraSequences(0).getData(PTASequenceEngine.truePred));
      Pair<Double,Long> randSeq = compareLang(from, to, sequences);
      outcome.setValue(LONG_V.DURATION_RAND, outcome.getValue(LONG_V.DURATION_RAND)+randSeq.secondElem);
      outcome.setValue(DOUBLE_V.ACCURACY_RAND, randSeq.firstElem);
    }
   
View Full Code Here
TOP

Related Classes of statechum.analysis.learning.rpnicore.RandomPathGenerator

  • edu.uci.ics.jung.algorithms.shortestpath.DijkstraDistance
  • statechum.analysis.learning.experiments.AccuracyAndQuestionsExperiment$RPNIEvaluator
  • statechum.analysis.learning.experiments.Benchmarklinear
  • statechum.analysis.learning.experiments.IncrementalAccuracyAndQuestionsExperiment$RPNIEvaluator
  • statechum.analysis.learning.experiments.IterativeEvaluatorExperiment$IterativeEvaluator
  • statechum.analysis.learning.experiments.mutation.DiffExperiments
  • statechum.analysis.learning.experiments.OldIncrementalAccuracyAndQuestionsExperiment$RPNIEvaluator
  • statechum.analysis.learning.experiments.PairSelection.ASE2014$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.Cav2014$EvaluationOfExisingLearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.Cav2014$LearnerRunner
    • 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.