Changeset 12641 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation

Timestamp:

07/07/15 13:34:55 (9 years ago)

Author:

gkronber

Message:

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

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation

Files:

• Classification/DiscriminantFunctionClassificationSolutionBase.cs (modified) (1 diff)
• Regression/RegressionSolutionBase.cs (modified) (1 diff)
• TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs (modified) (2 diffs)

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

@@ -105 +105 @@
       TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
 
-      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
+      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
 
       double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);

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

@@ -229 +229 @@
       TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
 
-      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
+      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
 
       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

@@ -394 +394 @@
       double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
       PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
-      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
+      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
       PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
@@ -430 +430 @@
       double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
+      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;