Free cookie consent management tool by TermsFeed Policy Generator

Ignore:
Timestamp:
11/08/11 10:13:21 (13 years ago)
Author:
mkommend
Message:

#1670: Corrected calculation of DataAnalysisSolution results and modified online calculators to have more meaningful parameter names.

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

Legend:

Unmodified
Added
Removed

  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs

    r6913 r6961  
    8484
    8585      OnlineCalculatorError errorState;
    86       double trainingAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState);
     86      double trainingAccuracy = OnlineAccuracyCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState);
    8787      if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN;
    88       double testAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState);
     88      double testAccuracy = OnlineAccuracyCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState);
    8989      if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN;
    9090

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

    r6913 r6961  
    108108
    109109      OnlineCalculatorError errorState;
    110       double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     110      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    111111      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
    112       double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     112      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    113113      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
    114114
    115       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     115      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    116116      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    117       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     117      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    118118      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
    119119

  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

    r6740 r6961  
    147147
    148148      OnlineCalculatorError errorState;
    149       double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     149      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    150150      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
    151       double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     151      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    152152      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
    153153
    154       double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     154      double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    155155      TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
    156       double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     156      double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    157157      TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    158158
    159       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     159      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    160160      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    161       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     161      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    162162      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
    163163
    164       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     164      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    165165      TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
    166       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     166      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    167167      TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
    168168
    169       double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     169      double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    170170      TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
    171       double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     171      double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    172172      TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
    173173    }

  • trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

    r6802 r6961  
    166166
    167167      OnlineCalculatorError errorState;
    168       double trainingMse = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     168      double trainingMse = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    169169      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMse : double.NaN;
    170       double testMse = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     170      double testMse = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    171171      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMse : double.NaN;
    172172
    173       double trainingMae = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     173      double trainingMae = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    174174      TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMae : double.NaN;
    175       double testMae = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     175      double testMae = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    176176      TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMae : double.NaN;
    177177
    178       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     178      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    179179      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    180       double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     180      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    181181      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
    182182
    183       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     183      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    184184      TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
    185       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     185      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    186186      TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
    187187
    188       double trainingNmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     188      double trainingNmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    189189      TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNmse : double.NaN;
    190       double testNmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     190      double testNmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    191191      TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNmse : double.NaN;
    192192
    193       double trainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     193      double trainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    194194      TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? trainingDirectionalSymmetry : double.NaN;
    195       double testDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     195      double testDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    196196      TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? testDirectionalSymmetry : double.NaN;
    197197
    198       double trainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     198      double trainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    199199      TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? trainingWeightedDirectionalSymmetry : double.NaN;
    200       double testWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     200      double testWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    201201      TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? testWeightedDirectionalSymmetry : double.NaN;
    202202
    203       double trainingTheilsU = OnlineTheilsUStatisticCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
     203      double trainingTheilsU = OnlineTheilsUStatisticCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    204204      TrainingTheilsUStatistic = errorState == OnlineCalculatorError.None ? trainingTheilsU : double.NaN;
    205       double testTheilsU = OnlineTheilsUStatisticCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
     205      double testTheilsU = OnlineTheilsUStatisticCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    206206      TestTheilsUStatistic = errorState == OnlineCalculatorError.None ? testTheilsU : double.NaN;
    207207
Note: See TracChangeset for help on using the changeset viewer.