Changeset 14000 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs

Timestamp:

07/05/16 14:05:46 (7 years ago)

Author:

mkommend

Message:

#2604: Removed default ctor arguments for the target variable in regression and classification models.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs (modified) (4 diffs)

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

@@ -373 +373 @@
       //mean model
       double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-      var meanModel = new ConstantModel(trainingMean);
+      var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
       //AR1 model
@@ -395 +395 @@
       PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
       double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
       PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
@@ -431 +431 @@
       PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
       double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
+      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
@@ -448 +448 @@
         //mean model
         double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-        var meanModel = new ConstantModel(trainingMean);
+        var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
         //AR1 model