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

Timestamp:

04/04/11 15:38:16 (14 years ago)

Author:

mkommend

Message:

#1453: Renamed IOnlineEvaluator to IOnlineCalculator

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4

Files:

• HeuristicLab.Problems.DataAnalysis-3.4.csproj (modified) (2 diffs)
• Implementation/Classification/ClassificationSolution.cs (modified) (1 diff)
• Implementation/Classification/DiscriminantFunctionClassificationSolution.cs (modified) (1 diff)
• Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs (modified) (1 diff)
• Implementation/Regression/RegressionSolution.cs (modified) (4 diffs)
• Interfaces/IOnlineCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/IOnlineEvaluator.cs) (2 diffs)
• OnlineEvaluators/OnlineAccuracyCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineAccuracyEvaluator.cs) (6 diffs)
• OnlineEvaluators/OnlineCovarianceCalcualtor.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineCovarianceEvaluator.cs) (6 diffs)
• OnlineEvaluators/OnlineLinearScalingParameterCalculator.cs (modified) (5 diffs)
• OnlineEvaluators/OnlineMeanAbsolutePercentageErrorCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanAbsolutePercentageErrorEvaluator.cs) (7 diffs)
• OnlineEvaluators/OnlineMeanAndVarianceCalculator.cs (modified) (6 diffs)
• OnlineEvaluators/OnlineMeanSquaredErrorCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanSquaredErrorEvaluator.cs) (5 diffs)
• OnlineEvaluators/OnlineNormalizedMeanSquaredErrorCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineNormalizedMeanSquaredErrorEvaluator.cs) (6 diffs)
• OnlineEvaluators/OnlinePearsonsRSquaredCalculator.cs (moved) (moved from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlinePearsonsRSquaredEvaluator.cs) (5 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj

@@ -130 +130 @@
     <Compile Include="Interfaces\Clustering\IClusteringProblemData.cs" />
     <Compile Include="Interfaces\Clustering\IClusteringSolution.cs" />
-    <Compile Include="OnlineEvaluators\OnlineAccuracyEvaluator.cs" />
+    <Compile Include="OnlineEvaluators\OnlineAccuracyCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionProblemData.cs" />
     <Compile Include="Implementation\DataAnalysisProblemData.cs" />
@@ -142 +142 @@
     <Compile Include="Interfaces\IDataAnalysisProblemData.cs" />
     <Compile Include="Interfaces\IDataAnalysisSolution.cs" />
-    <Compile Include="Interfaces\IOnlineEvaluator.cs" />
+    <Compile Include="Interfaces\IOnlineCalculator.cs" />
     <Compile Include="Interfaces\Regression\IRegressionModel.cs" />
     <Compile Include="Interfaces\Regression\IRegressionProblem.cs" />
     <Compile Include="Interfaces\Regression\IRegressionProblemData.cs" />
     <Compile Include="Interfaces\Regression\IRegressionSolution.cs" />
-    <Compile Include="OnlineEvaluators\OnlineCovarianceEvaluator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineMeanAbsolutePercentageErrorEvaluator.cs" />
+    <Compile Include="OnlineEvaluators\OnlineCovarianceCalcualtor.cs" />
+    <Compile Include="OnlineEvaluators\OnlineMeanAbsolutePercentageErrorCalculator.cs" />
     <Compile Include="OnlineEvaluators\OnlineMeanAndVarianceCalculator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineMeanSquaredErrorEvaluator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineNormalizedMeanSquaredErrorEvaluator.cs" />
-    <Compile Include="OnlineEvaluators\OnlinePearsonsRSquaredEvaluator.cs" />
+    <Compile Include="OnlineEvaluators\OnlineMeanSquaredErrorCalculator.cs" />
+    <Compile Include="OnlineEvaluators\OnlineNormalizedMeanSquaredErrorCalculator.cs" />
+    <Compile Include="OnlineEvaluators\OnlinePearsonsRSquaredCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionSolution.cs" />
     <Compile Include="TableFileParser.cs" />

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;
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/IOnlineCalculator.cs

@@ -24 +24 @@
 namespace HeuristicLab.Problems.DataAnalysis {
   [Flags]
-  public enum OnlineEvaluatorError { 
+  public enum OnlineCalculatorError { 
     /// <summary>
     /// No error occurred
@@ -38 +38 @@
     InsufficientElementsAdded = 2
   }
-  public interface IOnlineEvaluator {
-    OnlineEvaluatorError ErrorState { get; }
+  public interface IOnlineCalculator {
+    OnlineCalculatorError ErrorState { get; }
     double Value { get; }
     void Reset();

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineAccuracyCalculator.cs

@@ -21 +21 @@
 
 using System;
+using System.Collections.Generic;
 using HeuristicLab.Common;
-using System.Collections.Generic;
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineAccuracyEvaluator : IOnlineEvaluator {
+  public class OnlineAccuracyCalculator : IOnlineCalculator {
 
     private int correctlyClassified;
@@ -35 +35 @@
     }
 
-    public OnlineAccuracyEvaluator() {
+    public OnlineAccuracyCalculator() {
       Reset();
     }
 
-    #region IOnlineEvaluator Members
-    private OnlineEvaluatorError errorState;
-    public OnlineEvaluatorError ErrorState {
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
       get { return errorState; }
     }
@@ -50 +50 @@
       n = 0;
       correctlyClassified = 0;
-      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
 
@@ -63 +63 @@
           correctlyClassified++;
         }
-        errorState = OnlineEvaluatorError.None; // number of (non-NaN) samples >= 1
+        errorState = OnlineCalculatorError.None; // number of (non-NaN) samples >= 1
       }
     }
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineAccuracyEvaluator accuracyEvaluator = new OnlineAccuracyEvaluator();
+      OnlineAccuracyCalculator accuracyCalculator = new OnlineAccuracyCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -77 +77 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        accuracyEvaluator.Add(original, estimated);
+        accuracyCalculator.Add(original, estimated);
       }
 
@@ -84 +84 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = accuracyEvaluator.ErrorState;
-        return accuracyEvaluator.Accuracy;
+        errorState = accuracyCalculator.ErrorState;
+        return accuracyCalculator.Accuracy;
       }
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineCovarianceCalcualtor.cs

@@ -24 +24 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineCovarianceEvaluator : IOnlineEvaluator {
+  public class OnlineCovarianceCalculator : IOnlineCalculator {
 
     private double originalMean, estimatedMean, Cn;
@@ -34 +34 @@
     }
 
-    public OnlineCovarianceEvaluator() {
+    public OnlineCovarianceCalculator() {
       Reset();
     }
 
-    #region IOnlineEvaluator Members
-    private OnlineEvaluatorError errorState;
-    public OnlineEvaluatorError ErrorState {
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
       get { return errorState; }
     }
@@ -51 +51 @@
       originalMean = 0.0;
       estimatedMean = 0.0;
-      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
 
     public void Add(double original, double estimated) {
-      if (double.IsNaN(estimated) || double.IsInfinity(estimated) || double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineEvaluatorError.InvalidValueAdded) > 0) {
-        errorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
+      if (double.IsNaN(estimated) || double.IsInfinity(estimated) || double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
       } else {
         n++;
-        errorState = errorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 1
+        errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
 
         // online calculation of tMean
@@ -72 +72 @@
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineCovarianceEvaluator covarianceEvaluator = new OnlineCovarianceEvaluator();
+      OnlineCovarianceCalculator covarianceCalculator = new OnlineCovarianceCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -81 +81 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        covarianceEvaluator.Add(original, estimated);
+        covarianceCalculator.Add(original, estimated);
       }
 
@@ -88 +88 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = covarianceEvaluator.ErrorState;
-        return covarianceEvaluator.Covariance;
+        errorState = covarianceCalculator.ErrorState;
+        return covarianceCalculator.Covariance;
       }
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineLinearScalingParameterCalculator.cs

@@ -44 +44 @@
           return 1;
         else
-          return originalTargetCovarianceEvaluator.Covariance / originalMeanAndVarianceCalculator.PopulationVariance;
+          return originalTargetCovarianceCalculator.Covariance / originalMeanAndVarianceCalculator.PopulationVariance;
       }
     }
 
-    public OnlineEvaluatorError ErrorState {
+    public OnlineCalculatorError ErrorState {
       get {
         return targetMeanCalculator.MeanErrorState | originalMeanAndVarianceCalculator.MeanErrorState |
-          originalMeanAndVarianceCalculator.PopulationVarianceErrorState | originalTargetCovarianceEvaluator.ErrorState;
+          originalMeanAndVarianceCalculator.PopulationVarianceErrorState | originalTargetCovarianceCalculator.ErrorState;
       }
     }
@@ -58 +58 @@
     private OnlineMeanAndVarianceCalculator targetMeanCalculator;
     private OnlineMeanAndVarianceCalculator originalMeanAndVarianceCalculator;
-    private OnlineCovarianceEvaluator originalTargetCovarianceEvaluator;
+    private OnlineCovarianceCalculator originalTargetCovarianceCalculator;
 
     public OnlineLinearScalingParameterCalculator() {
       targetMeanCalculator = new OnlineMeanAndVarianceCalculator();
       originalMeanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
-      originalTargetCovarianceEvaluator = new OnlineCovarianceEvaluator();
+      originalTargetCovarianceCalculator = new OnlineCovarianceCalculator();
       Reset();
     }
@@ -71 +71 @@
       targetMeanCalculator.Reset();
       originalMeanAndVarianceCalculator.Reset();
-      originalTargetCovarianceEvaluator.Reset();
+      originalTargetCovarianceCalculator.Reset();
     }
 
@@ -83 +83 @@
       targetMeanCalculator.Add(target);
       originalMeanAndVarianceCalculator.Add(original);
-      originalTargetCovarianceEvaluator.Add(original, target);
+      originalTargetCovarianceCalculator.Add(original, target);
 
       cnt++;
@@ -97 +97 @@
     /// <param name="beta">Multiplicative factor for the linear scaling</param>
     /// <param name="errorState">Flag that indicates if errors occurred in the calculation of the linea scaling parameters.</param>
-    public static void Calculate(IEnumerable<double> original, IEnumerable<double> target, out double alpha, out double beta, out OnlineEvaluatorError errorState) {
+    public static void Calculate(IEnumerable<double> original, IEnumerable<double> target, out double alpha, out double beta, out OnlineCalculatorError errorState) {
       OnlineLinearScalingParameterCalculator calculator = new OnlineLinearScalingParameterCalculator();
       IEnumerator<double> originalEnumerator = original.GetEnumerator();

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanAbsolutePercentageErrorCalculator.cs

@@ -25 +25 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineMeanAbsolutePercentageErrorEvaluator : IOnlineEvaluator {
+  public class OnlineMeanAbsolutePercentageErrorCalculator : IOnlineCalculator {
 
     private double sre;
@@ -35 +35 @@
     }
 
-    public OnlineMeanAbsolutePercentageErrorEvaluator() {
+    public OnlineMeanAbsolutePercentageErrorCalculator() {
       Reset();
     }
 
-    #region IOnlineEvaluator Members
-    private OnlineEvaluatorError errorState;
-    public OnlineEvaluatorError ErrorState {
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
       get { return errorState; }
     }
@@ -50 +50 @@
       n = 0;
       sre = 0.0;
-      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
 
@@ -57 +57 @@
           double.IsNaN(original) || double.IsInfinity(original) ||
         original.IsAlmost(0.0)) {
-        errorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
       } else {
         sre += Math.Abs((estimated - original) / original);
         n++;
-        errorState = errorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 1
+        errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
       }
     }
@@ -67 +67 @@
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineMeanAbsolutePercentageErrorEvaluator evaluator = new OnlineMeanAbsolutePercentageErrorEvaluator();
+      OnlineMeanAbsolutePercentageErrorCalculator calculator = new OnlineMeanAbsolutePercentageErrorCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -76 +76 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        evaluator.Add(original, estimated);
+        calculator.Add(original, estimated);
       }
 
@@ -83 +83 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = evaluator.ErrorState;
-        return evaluator.MeanAbsolutePercentageError;
+        errorState = calculator.ErrorState;
+        return calculator.MeanAbsolutePercentageError;
       }
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanAndVarianceCalculator.cs

@@ -29 +29 @@
     private int n;
 
-    private OnlineEvaluatorError varianceErrorState;
-    public OnlineEvaluatorError VarianceErrorState {
+    private OnlineCalculatorError varianceErrorState;
+    public OnlineCalculatorError VarianceErrorState {
       get { return varianceErrorState; }
     }
@@ -40 +40 @@
     }
 
-    private OnlineEvaluatorError errorState;
-    public OnlineEvaluatorError PopulationVarianceErrorState {
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError PopulationVarianceErrorState {
       get { return errorState; }
     }
@@ -50 +50 @@
     }
 
-    public OnlineEvaluatorError MeanErrorState {
+    public OnlineCalculatorError MeanErrorState {
       get { return errorState; }
     }
@@ -69 +69 @@
     public void Reset() {
       n = 0;
-      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
-      varianceErrorState = OnlineEvaluatorError.InsufficientElementsAdded;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+      varianceErrorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
 
     public void Add(double x) {
-      if (double.IsNaN(x) || double.IsInfinity(x) || (errorState & OnlineEvaluatorError.InvalidValueAdded) > 0) {
-        errorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
-        varianceErrorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
+      if (double.IsNaN(x) || double.IsInfinity(x) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+        varianceErrorState = errorState | OnlineCalculatorError.InvalidValueAdded;
       } else {
         n++;
@@ -83 +83 @@
           m_oldM = m_newM = x;
           m_oldS = 0.0;
-          errorState = errorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 1
+          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
         } else {
-          varianceErrorState = varianceErrorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 2
+          varianceErrorState = varianceErrorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 2
           m_newM = m_oldM + (x - m_oldM) / n;
           m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
@@ -96 +96 @@
     }
 
-    public static void Calculate(IEnumerable<double> x, out double mean, out double variance, out OnlineEvaluatorError meanErrorState, out OnlineEvaluatorError varianceErrorState) {
+    public static void Calculate(IEnumerable<double> x, out double mean, out double variance, out OnlineCalculatorError meanErrorState, out OnlineCalculatorError varianceErrorState) {
       OnlineMeanAndVarianceCalculator meanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
       foreach (double xi in x) {

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanSquaredErrorCalculator.cs

@@ -24 +24 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineMeanSquaredErrorEvaluator : IOnlineEvaluator {
+  public class OnlineMeanSquaredErrorCalculator : IOnlineCalculator {
 
     private double sse;
@@ -34 +34 @@
     }
 
-    public OnlineMeanSquaredErrorEvaluator() {
+    public OnlineMeanSquaredErrorCalculator() {
       Reset();
     }
 
-    #region IOnlineEvaluator Members
-    private OnlineEvaluatorError errorState;
-    public OnlineEvaluatorError ErrorState {
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
       get { return errorState; }
     }
@@ -49 +49 @@
       n = 0;
       sse = 0.0;
-      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
 
     public void Add(double original, double estimated) {
       if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
-          double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineEvaluatorError.InvalidValueAdded) > 0) {
-        errorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
+          double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
       } else {
         double error = estimated - original;
         sse += error * error;
         n++;
-        errorState = errorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 1
+        errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
       }
     }
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
+      OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -74 +74 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        mseEvaluator.Add(original, estimated);
+        mseCalculator.Add(original, estimated);
       }
 
@@ -81 +81 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = mseEvaluator.ErrorState;
-        return mseEvaluator.MeanSquaredError;
+        errorState = mseCalculator.ErrorState;
+        return mseCalculator.MeanSquaredError;
       }
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineNormalizedMeanSquaredErrorCalculator.cs

@@ -24 +24 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineNormalizedMeanSquaredErrorEvaluator : IOnlineEvaluator {
+  public class OnlineNormalizedMeanSquaredErrorCalculator : IOnlineCalculator {
     private OnlineMeanAndVarianceCalculator meanSquaredErrorCalculator;
     private OnlineMeanAndVarianceCalculator originalVarianceCalculator;
@@ -36 +36 @@
     }
 
-    public OnlineNormalizedMeanSquaredErrorEvaluator() {
+    public OnlineNormalizedMeanSquaredErrorCalculator() {
       meanSquaredErrorCalculator = new OnlineMeanAndVarianceCalculator();
       originalVarianceCalculator = new OnlineMeanAndVarianceCalculator();
@@ -42 +42 @@
     }
 
-    #region IOnlineEvaluator Members
-    public OnlineEvaluatorError ErrorState {
+    #region IOnlineCalculator Members
+    public OnlineCalculatorError ErrorState {
       get { return meanSquaredErrorCalculator.MeanErrorState | originalVarianceCalculator.VarianceErrorState; }
     }
@@ -63 +63 @@
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineNormalizedMeanSquaredErrorEvaluator normalizedMSEEvaluator = new OnlineNormalizedMeanSquaredErrorEvaluator();
+      OnlineNormalizedMeanSquaredErrorCalculator normalizedMSECalculator = new OnlineNormalizedMeanSquaredErrorCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -72 +72 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        normalizedMSEEvaluator.Add(original, estimated);
+        normalizedMSECalculator.Add(original, estimated);
       }
 
@@ -79 +79 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = normalizedMSEEvaluator.ErrorState;
-        return normalizedMSEEvaluator.NormalizedMeanSquaredError;
+        errorState = normalizedMSECalculator.ErrorState;
+        return normalizedMSECalculator.NormalizedMeanSquaredError;
       }
     }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlinePearsonsRSquaredCalculator.cs

@@ -25 +25 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlinePearsonsRSquaredEvaluator : IOnlineEvaluator {
-    private OnlineCovarianceEvaluator covEvaluator = new OnlineCovarianceEvaluator();
-    private OnlineMeanAndVarianceCalculator sxEvaluator = new OnlineMeanAndVarianceCalculator();
-    private OnlineMeanAndVarianceCalculator syEvaluator = new OnlineMeanAndVarianceCalculator();
+  public class OnlinePearsonsRSquaredCalculator : IOnlineCalculator {
+    private OnlineCovarianceCalculator covCalculator = new OnlineCovarianceCalculator();
+    private OnlineMeanAndVarianceCalculator sxCalculator = new OnlineMeanAndVarianceCalculator();
+    private OnlineMeanAndVarianceCalculator syCalculator = new OnlineMeanAndVarianceCalculator();
 
     public double RSquared {
       get {
-        double xVar = sxEvaluator.PopulationVariance;
-        double yVar = syEvaluator.PopulationVariance;
+        double xVar = sxCalculator.PopulationVariance;
+        double yVar = syCalculator.PopulationVariance;
         if (xVar.IsAlmost(0.0) || yVar.IsAlmost(0.0)) {
           return 0.0;
         } else {
-          double r = covEvaluator.Covariance / (Math.Sqrt(xVar) * Math.Sqrt(yVar));
+          double r = covCalculator.Covariance / (Math.Sqrt(xVar) * Math.Sqrt(yVar));
           return r * r;
         }
@@ -43 +43 @@
     }
 
-    public OnlinePearsonsRSquaredEvaluator() { }
+    public OnlinePearsonsRSquaredCalculator() { }
 
-    #region IOnlineEvaluator Members
-    public OnlineEvaluatorError ErrorState {
-      get { return covEvaluator.ErrorState | sxEvaluator.PopulationVarianceErrorState | syEvaluator.PopulationVarianceErrorState; }
+    #region IOnlineCalculator Members
+    public OnlineCalculatorError ErrorState {
+      get { return covCalculator.ErrorState | sxCalculator.PopulationVarianceErrorState | syCalculator.PopulationVarianceErrorState; }
     }
     public double Value {
@@ -53 +53 @@
     }
     public void Reset() {
-      covEvaluator.Reset();
-      sxEvaluator.Reset();
-      syEvaluator.Reset();
+      covCalculator.Reset();
+      sxCalculator.Reset();
+      syCalculator.Reset();
     }
 
     public void Add(double x, double y) {
       // no need to check validity of values explicitly here as it is checked in all three evaluators 
-      covEvaluator.Add(x, y);
-      sxEvaluator.Add(x);
-      syEvaluator.Add(y);
+      covCalculator.Add(x, y);
+      sxCalculator.Add(x);
+      syCalculator.Add(y);
     }
 
     #endregion
 
-    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineEvaluatorError errorState) {
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlinePearsonsRSquaredEvaluator rSquaredEvaluator = new OnlinePearsonsRSquaredEvaluator();
+      OnlinePearsonsRSquaredCalculator rSquaredCalculator = new OnlinePearsonsRSquaredCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -76 +76 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        rSquaredEvaluator.Add(original, estimated);
+        rSquaredCalculator.Add(original, estimated);
       }
 
@@ -83 +83 @@
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = rSquaredEvaluator.ErrorState;
-        return rSquaredEvaluator.RSquared;
+        errorState = rSquaredCalculator.ErrorState;
+        return rSquaredCalculator.RSquared;
       }
     }