Ignore:
Timestamp:
07/07/15 13:34:55 (7 years ago)
Author:
gkronber
Message:

#2392: fixed all warnings by using PearsonsRCalculator instead of PearsonsRSquaredCalculator

Location:
trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4
Files:
4 edited

Legend:

Unmodified
Added
Removed
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

    r12012 r12641  
    105105      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
    106106
    107       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    108       TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    109       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    110       TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     107      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     108      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
     109      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     110      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    111111
    112112      double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

    r12581 r12641  
    229229      TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    230230
    231       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    232       TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    233       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    234       TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     231      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     232      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
     233      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     234      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    235235
    236236      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

    r12012 r12641  
    394394      double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    395395      PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
    396       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    397       PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
     396      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     397      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
    398398      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    399399      PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
     
    430430      double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    431431      PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    432       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    433       PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     432      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     433      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
    434434      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    435435      PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/DependencyCalculator/PearsonsRSquaredDependenceCalculator.cs

    r12012 r12641  
    3232
    3333    public double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
    34       return OnlinePearsonsRSquaredCalculator.Calculate(originalValues, estimatedValues, out errorState);
     34      var r = OnlinePearsonsRCalculator.Calculate(originalValues, estimatedValues, out errorState);
     35      return r * r;
    3536    }
    3637  }
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