- Timestamp:
- 11/08/11 10:13:21 (13 years ago)
- File:
-
- 1 edited
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trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs
r6740 r6961 147 147 148 148 OnlineCalculatorError errorState; 149 double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate( estimatedTrainingValues, originalTrainingValues, out errorState);149 double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState); 150 150 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); 152 152 TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN; 153 153 154 double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate( estimatedTrainingValues, originalTrainingValues, out errorState);154 double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState); 155 155 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); 157 157 TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN; 158 158 159 double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate( estimatedTrainingValues, originalTrainingValues, out errorState);159 double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState); 160 160 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); 162 162 TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN; 163 163 164 double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate( estimatedTrainingValues, originalTrainingValues, out errorState);164 double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState); 165 165 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); 167 167 TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN; 168 168 169 double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate( estimatedTrainingValues, originalTrainingValues, out errorState);169 double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState); 170 170 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); 172 172 TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN; 173 173 }
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