Changeset 5942 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation

Timestamp:

04/04/11 15:38:16 (13 years ago)

Author:

mkommend

Message:

#1453: Renamed IOnlineEvaluator to IOnlineCalculator

Location:

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

Files:

• Classification/ClassificationSolution.cs (modified) (1 diff)
• Classification/DiscriminantFunctionClassificationSolution.cs (modified) (1 diff)
• Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs (modified) (1 diff)
• Regression/RegressionSolution.cs (modified) (4 diffs)

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

@@ -85 +85 @@
       IEnumerable<double> originalTestClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
 
-      OnlineEvaluatorError errorState;
-      double trainingAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) trainingAccuracy = double.NaN;
-      double testAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState);
-      if (errorState != OnlineEvaluatorError.None) testAccuracy = double.NaN;
+      OnlineCalculatorError errorState;
+      double trainingAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN;
+      double testAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState);
+      if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN;
 
       TrainingAccuracy = trainingAccuracy;

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

@@ -105 +105 @@
       IEnumerable<double> originalTestValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
 
-      OnlineEvaluatorError errorState;
-      double trainingMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
-      TrainingMeanSquaredError = errorState == OnlineEvaluatorError.None ? trainingMSE : double.NaN;
-      double testMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTestValues, originalTestValues, out errorState);
-      TestMeanSquaredError = errorState == OnlineEvaluatorError.None ? testMSE : double.NaN;
+      OnlineCalculatorError errorState;
+      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
+      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
 
-      double trainingR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineEvaluatorError.None ? trainingR2 : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTestValues, originalTestValues, out errorState);
-      TestRSquared = errorState == OnlineEvaluatorError.None ? testR2 : double.NaN;
+      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
+      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs

@@ -64 +64 @@
         double classValue = group.Key;
         double mean, variance;
-        OnlineEvaluatorError meanErrorState, varianceErrorState;
+        OnlineCalculatorError meanErrorState, varianceErrorState;
         OnlineMeanAndVarianceCalculator.Calculate(estimatedClassValues, out mean, out variance, out meanErrorState, out varianceErrorState);
 
-        if (meanErrorState == OnlineEvaluatorError.None && varianceErrorState == OnlineEvaluatorError.None) {
+        if (meanErrorState == OnlineCalculatorError.None && varianceErrorState == OnlineCalculatorError.None) {
           classMean[classValue] = mean;
           classStdDev[classValue] = Math.Sqrt(variance);

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

@@ -40 +40 @@
     private const string TrainingRelativeErrorResultName = "Average relative error (training)";
     private const string TestRelativeErrorResultName = "Average relative error (test)";
+    private const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
+    private const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
 
     public new IRegressionModel Model {
@@ -81 +83 @@
     }
 
+    public double TrainingNormalizedMeanSquaredError {
+      get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
+    }
+
+    public double TestNormalizedMeanSquaredError {
+      get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
+    }
+
 
     [StorableConstructor]
@@ -95 +107 @@
       Add(new Result(TrainingRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the training partition", new PercentValue()));
       Add(new Result(TestRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the test partition", new PercentValue()));
+      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "", new DoubleValue()));
+      Add(new Result(TestNormalizedMeanSquaredErrorResultName, "", new DoubleValue()));
 
       RecalculateResults();
@@ -114 +128 @@
       IEnumerable<double> originalTestValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
 
-      OnlineEvaluatorError errorState;
-      double trainingMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
-      TrainingMeanSquaredError = errorState == OnlineEvaluatorError.None ? trainingMSE : double.NaN;
-      double testMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTestValues, originalTestValues, out errorState);
-      TestMeanSquaredError = errorState == OnlineEvaluatorError.None ? testMSE : double.NaN;
+      OnlineCalculatorError errorState;
+      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
+      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
 
-      double trainingR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineEvaluatorError.None ? trainingR2 : double.NaN;
-      double testR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTestValues, originalTestValues, out errorState);
-      TestRSquared = errorState == OnlineEvaluatorError.None ? testR2 : double.NaN;
+      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
+      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
 
-      double trainingRelError = OnlineMeanAbsolutePercentageErrorEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
-      TrainingRelativeError = errorState == OnlineEvaluatorError.None ? trainingRelError : double.NaN;
-      double testRelError = OnlineMeanAbsolutePercentageErrorEvaluator.Calculate(estimatedTestValues, originalTestValues, out errorState);
-      TestRelativeError = errorState == OnlineEvaluatorError.None ? testRelError : double.NaN;
+      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
+      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
+
+      double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
+      double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
     }