Package statechum.analysis.learning.PrecisionRecall

Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix

396
397
398
399
400
401
402
      else if(!inTarget && inMutated)
        fp++;
      else if(!inTarget && !inMutated)
        tn++;
    }
    return new ConfusionMatrix(tp,tn,fp,fn);
  }
View Full Code Here
425
426
427
428
429
430
431
      else if(!inFirst && inSecond)
        fp++;
      else if(!inFirst && !inSecond)
        tn++;
    }
    return new ConfusionMatrix(tp,tn,fp,fn);
  } 
View Full Code Here
560
561
562
563
564
565
566
567
    set.clear();
    set.addAll(expected);
    set.removeAll(detected);
    fn = set.size();
   
    ConfusionMatrix conf = new ConfusionMatrix(tp, tn, fp, fn);
    return conf.fMeasure();
  }
View Full Code Here
430
431
432
433
434
435
436
437
438
439
440
        dataSample.miscGraphs.put("E>=2",dataSample.EDSMtwo);

       

        {// 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, trimmedReference, m);
          dataSample.markovLearner = new ScoresForGraph();     
//          System.out.println("Markov");
          dataSample.markovLearner.differenceBCR = new DifferenceToReferenceLanguageBCR(mat);
//          System.out.println(dataSample.markovLearner.differenceBCR.getValue());
          dataSample.miscGraphs.put("M",dataSample.markovLearner);
View Full Code Here
16
17
18
19
20
21
22
23
24
25
26
27
28
29
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to= buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"a"}
    },config), from, to);
    Assert.assertEquals(1.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(1.,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(1.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
33
34
35
36
37
38
39
40
41
42
43
44
45
46
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to=buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"notransition"}
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(1.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
50
51
52
53
54
55
56
57
58
59
60
61
62
63
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to=buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"c"}
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(1.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
67
68
69
70
71
72
73
74
75
76
77
78
79
80
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to=buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"a","a"} // FN
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to=buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"a","a"} // FN
        ,new String[]{"b"} // TP
    },config), from, to);
    Assert.assertEquals(1.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0.5,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.66666666,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0.25,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
  {
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraph from = buildLearnerGraph("A-a->A-b->B / A-c-#C","testClassify1a",config),
    to=buildLearnerGraph("A-b->A-a->B","testClassify1b",config);
   
    ConfusionMatrix matrix = DiffExperiments.classify(TestFSMAlgo.buildSet(new String[][]{
        new String[]{"a","a"} // FN
        ,new String[]{"b","b"} // FP
    },config), from, to);
    Assert.assertEquals(0.,matrix.getPrecision(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.getRecall(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.fMeasure(),Configuration.fpAccuracy);
    Assert.assertEquals(0.,matrix.getSpecificity(),Configuration.fpAccuracy);
    Assert.assertEquals(0,matrix.BCR(),Configuration.fpAccuracy);
  }
View Full Code Here
TOP

Related Classes of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix

  • statechum.analysis.Erlang.Synapse$StatechumProcess
  • statechum.analysis.learning.experiments.mutation.DiffExperiments
  • statechum.analysis.learning.experiments.mutation.TestDiffExperiments
  • statechum.analysis.learning.experiments.PairSelection.CVS$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.CVS_With_Random_traces_Generation$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.MarkovLearnerExperimentWithStatisticalAnalysis$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.PairQualityLearner$DifferenceToReferenceFMeasure
  • statechum.analysis.learning.experiments.PairSelection.PairQualityLearner$DifferenceToReferenceLanguage
  • statechum.analysis.learning.experiments.PairSelection.PairQualityLearner$DifferenceToReferenceLanguageBCR
  • statechum.analysis.learning.experiments.PaperUAS
    • 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.