Package statechum.analysis.learning.PrecisionRecall

Examples of statechum.analysis.learning.PrecisionRecall.ConfusionMatrix

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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceFMeasure estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceFMeasure(mat);
    }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceLanguageBCR estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceLanguageBCR(mat);
    }
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  private Pair<Double,Long> compareLang(LearnerGraph from, LearnerGraph to,
      Collection<List<Label>> sequences)
  {
   
    final long startTime = System.nanoTime();
    ConfusionMatrix matrix = classify(sequences, from,to);
    final long duration = System.nanoTime() - startTime;
    double result = matrix.fMeasure();
    assert !Double.isNaN(result);
    return new Pair<Double,Long>(result,duration);
  }
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      else if(!inTarget && inMutated)
        fp++;
      else if(!inTarget && !inMutated)
        tn++;
    }
    return new ConfusionMatrix(tp,tn,fp,fn);
  }
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    set.clear();
    set.addAll(expected);
    set.removeAll(detected);
    fn = set.size();
   
    ConfusionMatrix conf = new ConfusionMatrix(tp, tn, fp, fn);
    return conf.fMeasure();
  }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceFMeasure estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceFMeasure(mat);
    }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceLanguageBCR estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceLanguageBCR(mat);
    }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceLanguage estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceLanguage(mat);
    }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceLanguageBCR estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceLanguageBCR(mat);
    }
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     * @param cpuNumber the number of processors to use. Usually set to 1 because we run as many experiments as there are CPUs and so individual experiments should not consume more computational power than we have available for them.
     */
    public static DifferenceToReferenceLanguage estimationOfDifference(LearnerGraph referenceGraph, LearnerGraph actualAutomaton, Collection<List<Label>> testSet)
    {
           LearnerGraph learntGraph = new LearnerGraph(actualAutomaton.config);AbstractPathRoutines.removeRejectStates(actualAutomaton,learntGraph);
           ConfusionMatrix mat = DiffExperiments.classify(testSet, referenceGraph, learntGraph);
      return new DifferenceToReferenceLanguage(mat);
    }
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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
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