Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj	(revision 5942)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj	(revision 5943)
@@ -119,5 +119,5 @@
     <Compile Include="Interfaces\Classification\IClassificationEnsembleModel.cs" />
     <Compile Include="Interfaces\Regression\IRegressionEnsembleModel.cs" />
-    <Compile Include="OnlineEvaluators\OnlineLinearScalingParameterCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineLinearScalingParameterCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionEnsembleModel.cs" />
     <Compile Include="Implementation\Classification\DiscriminantFunctionClassificationModel.cs" />
@@ -130,5 +130,5 @@
     <Compile Include="Interfaces\Clustering\IClusteringProblemData.cs" />
     <Compile Include="Interfaces\Clustering\IClusteringSolution.cs" />
-    <Compile Include="OnlineEvaluators\OnlineAccuracyCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineAccuracyCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionProblemData.cs" />
     <Compile Include="Implementation\DataAnalysisProblemData.cs" />
@@ -147,10 +147,10 @@
     <Compile Include="Interfaces\Regression\IRegressionProblemData.cs" />
     <Compile Include="Interfaces\Regression\IRegressionSolution.cs" />
-    <Compile Include="OnlineEvaluators\OnlineCovarianceCalcualtor.cs" />
-    <Compile Include="OnlineEvaluators\OnlineMeanAbsolutePercentageErrorCalculator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineMeanAndVarianceCalculator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineMeanSquaredErrorCalculator.cs" />
-    <Compile Include="OnlineEvaluators\OnlineNormalizedMeanSquaredErrorCalculator.cs" />
-    <Compile Include="OnlineEvaluators\OnlinePearsonsRSquaredCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineCovarianceCalcualtor.cs" />
+    <Compile Include="OnlineCalculators\OnlineMeanAbsolutePercentageErrorCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineMeanAndVarianceCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineMeanSquaredErrorCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineNormalizedMeanSquaredErrorCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlinePearsonsRSquaredCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionSolution.cs" />
     <Compile Include="TableFileParser.cs" />
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineAccuracyCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineAccuracyCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineAccuracyCalculator.cs	(revision 5943)
@@ -0,0 +1,91 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineAccuracyCalculator : IOnlineCalculator {
+
+    private int correctlyClassified;
+    private int n;
+    public double Accuracy {
+      get {
+        return correctlyClassified / (double)n;
+      }
+    }
+
+    public OnlineAccuracyCalculator() {
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+    public double Value {
+      get { return Accuracy; }
+    }
+    public void Reset() {
+      n = 0;
+      correctlyClassified = 0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double original, double estimated) {
+      // ignore cases where original is NaN completly 
+      if (!double.IsNaN(original)) {
+        // increment number of observed samples
+        n++;
+        if (original.IsAlmost(estimated)) {
+          // original = estimated = +Inf counts as correctly classified
+          // original = estimated = -Inf counts as correctly classified
+          correctlyClassified++;
+        }
+        errorState = OnlineCalculatorError.None; // number of (non-NaN) samples >= 1
+      }
+    }
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlineAccuracyCalculator accuracyCalculator = new OnlineAccuracyCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        accuracyCalculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = accuracyCalculator.ErrorState;
+        return accuracyCalculator.Accuracy;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineCovarianceCalcualtor.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineCovarianceCalcualtor.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineCovarianceCalcualtor.cs	(revision 5943)
@@ -0,0 +1,95 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineCovarianceCalculator : IOnlineCalculator {
+
+    private double originalMean, estimatedMean, Cn;
+    private int n;
+    public double Covariance {
+      get {
+        return n > 0 ? Cn / n : 0.0;
+      }
+    }
+
+    public OnlineCovarianceCalculator() {
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+    public double Value {
+      get { return Covariance; }
+    }
+    public void Reset() {
+      n = 0;
+      Cn = 0.0;
+      originalMean = 0.0;
+      estimatedMean = 0.0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double original, double estimated) {
+      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 & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
+
+        // online calculation of tMean
+        originalMean = originalMean + (original - originalMean) / n;
+        double delta = estimated - estimatedMean; // delta = (y - yMean(n-1))
+        estimatedMean = estimatedMean + delta / n;
+
+        // online calculation of covariance
+        Cn = Cn + delta * (original - originalMean); // C(n) = C(n-1) + (y - yMean(n-1)) (t - tMean(n))       
+      }
+    }
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlineCovarianceCalculator covarianceCalculator = new OnlineCovarianceCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        covarianceCalculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = covarianceCalculator.ErrorState;
+        return covarianceCalculator.Covariance;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineLinearScalingParameterCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineLinearScalingParameterCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineLinearScalingParameterCalculator.cs	(revision 5943)
@@ -0,0 +1,121 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineLinearScalingParameterCalculator {
+
+    /// <summary>
+    /// Additive constant
+    /// </summary>
+    public double Alpha {
+      get {
+        return targetMeanCalculator.Mean - Beta * originalMeanAndVarianceCalculator.Mean;
+      }
+    }
+
+    /// <summary>
+    /// Multiplicative factor
+    /// </summary>
+    public double Beta {
+      get {
+        if (originalMeanAndVarianceCalculator.PopulationVariance.IsAlmost(0.0))
+          return 1;
+        else
+          return originalTargetCovarianceCalculator.Covariance / originalMeanAndVarianceCalculator.PopulationVariance;
+      }
+    }
+
+    public OnlineCalculatorError ErrorState {
+      get {
+        return targetMeanCalculator.MeanErrorState | originalMeanAndVarianceCalculator.MeanErrorState |
+          originalMeanAndVarianceCalculator.PopulationVarianceErrorState | originalTargetCovarianceCalculator.ErrorState;
+      }
+    }
+
+    private int cnt;
+    private OnlineMeanAndVarianceCalculator targetMeanCalculator;
+    private OnlineMeanAndVarianceCalculator originalMeanAndVarianceCalculator;
+    private OnlineCovarianceCalculator originalTargetCovarianceCalculator;
+
+    public OnlineLinearScalingParameterCalculator() {
+      targetMeanCalculator = new OnlineMeanAndVarianceCalculator();
+      originalMeanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
+      originalTargetCovarianceCalculator = new OnlineCovarianceCalculator();
+      Reset();
+    }
+
+    public void Reset() {
+      cnt = 0;
+      targetMeanCalculator.Reset();
+      originalMeanAndVarianceCalculator.Reset();
+      originalTargetCovarianceCalculator.Reset();
+    }
+
+    /// <summary>
+    /// Calculates linear scaling parameters in one pass.
+    /// The formulas to calculate the scaling parameters were taken from Scaled Symblic Regression by Maarten Keijzer.
+    /// http://www.springerlink.com/content/x035121165125175/
+    /// </summary>
+    public void Add(double original, double target) {
+      // validity of values is checked in mean calculator and covariance calculator
+      targetMeanCalculator.Add(target);
+      originalMeanAndVarianceCalculator.Add(original);
+      originalTargetCovarianceCalculator.Add(original, target);
+
+      cnt++;
+    }
+
+    /// <summary>
+    /// Calculates alpha and beta parameters to linearly scale elements of original to the scale and location of target
+    /// original[i] * beta + alpha
+    /// </summary>
+    /// <param name="original">Values that should be scaled</param>
+    /// <param name="target">Target values to which the original values should be scaled</param>
+    /// <param name="alpha">Additive constant for the linear scaling</param>
+    /// <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 OnlineCalculatorError errorState) {
+      OnlineLinearScalingParameterCalculator calculator = new OnlineLinearScalingParameterCalculator();
+      IEnumerator<double> originalEnumerator = original.GetEnumerator();
+      IEnumerator<double> targetEnumerator = target.GetEnumerator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (originalEnumerator.MoveNext() & targetEnumerator.MoveNext()) {
+        double originalElement = originalEnumerator.Current;
+        double targetElement = targetEnumerator.Current;
+        calculator.Add(originalElement, targetElement);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (originalEnumerator.MoveNext() || targetEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in original and target enumeration do not match.");
+      } else {
+        errorState = calculator.ErrorState;
+        alpha = calculator.Alpha;
+        beta = calculator.Beta;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAbsolutePercentageErrorCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAbsolutePercentageErrorCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAbsolutePercentageErrorCalculator.cs	(revision 5943)
@@ -0,0 +1,90 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineMeanAbsolutePercentageErrorCalculator : IOnlineCalculator {
+
+    private double sre;
+    private int n;
+    public double MeanAbsolutePercentageError {
+      get {
+        return n > 0 ? sre / n : 0.0;
+      }
+    }
+
+    public OnlineMeanAbsolutePercentageErrorCalculator() {
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+    public double Value {
+      get { return MeanAbsolutePercentageError; }
+    }
+    public void Reset() {
+      n = 0;
+      sre = 0.0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double original, double estimated) {
+      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
+          double.IsNaN(original) || double.IsInfinity(original) ||
+        original.IsAlmost(0.0)) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        sre += Math.Abs((estimated - original) / original);
+        n++;
+        errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
+      }
+    }
+
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlineMeanAbsolutePercentageErrorCalculator calculator = new OnlineMeanAbsolutePercentageErrorCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        calculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = calculator.ErrorState;
+        return calculator.MeanAbsolutePercentageError;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAndVarianceCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAndVarianceCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAndVarianceCalculator.cs	(revision 5943)
@@ -0,0 +1,109 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineMeanAndVarianceCalculator {
+
+    private double m_oldM, m_newM, m_oldS, m_newS;
+    private int n;
+
+    private OnlineCalculatorError varianceErrorState;
+    public OnlineCalculatorError VarianceErrorState {
+      get { return varianceErrorState; }
+    }
+
+    public double Variance {
+      get {
+        return (n > 1) ? m_newS / (n - 1) : 0.0;
+      }
+    }
+
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError PopulationVarianceErrorState {
+      get { return errorState; }
+    }
+    public double PopulationVariance {
+      get {
+        return (n > 0) ? m_newS / n : 0.0;
+      }
+    }
+
+    public OnlineCalculatorError MeanErrorState {
+      get { return errorState; }
+    }
+    public double Mean {
+      get {
+        return (n > 0) ? m_newM : 0.0;
+      }
+    }
+
+    public int Count {
+      get { return n; }
+    }
+
+    public OnlineMeanAndVarianceCalculator() {
+      Reset();
+    }
+
+    public void Reset() {
+      n = 0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+      varianceErrorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double x) {
+      if (double.IsNaN(x) || double.IsInfinity(x) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+        varianceErrorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        n++;
+        // See Knuth TAOCP vol 2, 3rd edition, page 232
+        if (n == 1) {
+          m_oldM = m_newM = x;
+          m_oldS = 0.0;
+          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
+        } else {
+          varianceErrorState = varianceErrorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 2
+          m_newM = m_oldM + (x - m_oldM) / n;
+          m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
+
+          // set up for next iteration
+          m_oldM = m_newM;
+          m_oldS = m_newS;
+        }
+      }
+    }
+
+    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) {
+        meanAndVarianceCalculator.Add(xi);
+      }
+      mean = meanAndVarianceCalculator.Mean;
+      variance = meanAndVarianceCalculator.Variance;
+      meanErrorState = meanAndVarianceCalculator.MeanErrorState;
+      varianceErrorState = meanAndVarianceCalculator.VarianceErrorState;
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanSquaredErrorCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanSquaredErrorCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanSquaredErrorCalculator.cs	(revision 5943)
@@ -0,0 +1,88 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineMeanSquaredErrorCalculator : IOnlineCalculator {
+
+    private double sse;
+    private int n;
+    public double MeanSquaredError {
+      get {
+        return n > 0 ? sse / n : 0.0;
+      }
+    }
+
+    public OnlineMeanSquaredErrorCalculator() {
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+    public double Value {
+      get { return MeanSquaredError; }
+    }
+    public void Reset() {
+      n = 0;
+      sse = 0.0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double original, double estimated) {
+      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
+          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 & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
+      }
+    }
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        mseCalculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = mseCalculator.ErrorState;
+        return mseCalculator.MeanSquaredError;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineNormalizedMeanSquaredErrorCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineNormalizedMeanSquaredErrorCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineNormalizedMeanSquaredErrorCalculator.cs	(revision 5943)
@@ -0,0 +1,86 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineNormalizedMeanSquaredErrorCalculator : IOnlineCalculator {
+    private OnlineMeanAndVarianceCalculator meanSquaredErrorCalculator;
+    private OnlineMeanAndVarianceCalculator originalVarianceCalculator;
+
+    public double NormalizedMeanSquaredError {
+      get {
+        double var = originalVarianceCalculator.Variance;
+        double m = meanSquaredErrorCalculator.Mean;
+        return var > 0 ? m / var : 0.0;
+      }
+    }
+
+    public OnlineNormalizedMeanSquaredErrorCalculator() {
+      meanSquaredErrorCalculator = new OnlineMeanAndVarianceCalculator();
+      originalVarianceCalculator = new OnlineMeanAndVarianceCalculator();
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    public OnlineCalculatorError ErrorState {
+      get { return meanSquaredErrorCalculator.MeanErrorState | originalVarianceCalculator.VarianceErrorState; }
+    }
+    public double Value {
+      get { return NormalizedMeanSquaredError; }
+    }
+
+    public void Reset() {
+      meanSquaredErrorCalculator.Reset();
+      originalVarianceCalculator.Reset();
+    }
+
+    public void Add(double original, double estimated) {
+      // no need to check for validity of values explicitly as it is checked in the meanAndVariance calculator anyway
+      double error = estimated - original;
+      meanSquaredErrorCalculator.Add(error * error);
+      originalVarianceCalculator.Add(original);
+    }
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlineNormalizedMeanSquaredErrorCalculator normalizedMSECalculator = new OnlineNormalizedMeanSquaredErrorCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        normalizedMSECalculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = normalizedMSECalculator.ErrorState;
+        return normalizedMSECalculator.NormalizedMeanSquaredError;
+      }
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlinePearsonsRSquaredCalculator.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlinePearsonsRSquaredCalculator.cs	(revision 5943)
+++ trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlinePearsonsRSquaredCalculator.cs	(revision 5943)
@@ -0,0 +1,90 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  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 = sxCalculator.PopulationVariance;
+        double yVar = syCalculator.PopulationVariance;
+        if (xVar.IsAlmost(0.0) || yVar.IsAlmost(0.0)) {
+          return 0.0;
+        } else {
+          double r = covCalculator.Covariance / (Math.Sqrt(xVar) * Math.Sqrt(yVar));
+          return r * r;
+        }
+      }
+    }
+
+    public OnlinePearsonsRSquaredCalculator() { }
+
+    #region IOnlineCalculator Members
+    public OnlineCalculatorError ErrorState {
+      get { return covCalculator.ErrorState | sxCalculator.PopulationVarianceErrorState | syCalculator.PopulationVarianceErrorState; }
+    }
+    public double Value {
+      get { return RSquared; }
+    }
+    public void 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 
+      covCalculator.Add(x, y);
+      sxCalculator.Add(x);
+      syCalculator.Add(y);
+    }
+
+    #endregion
+
+    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second, out OnlineCalculatorError errorState) {
+      IEnumerator<double> firstEnumerator = first.GetEnumerator();
+      IEnumerator<double> secondEnumerator = second.GetEnumerator();
+      OnlinePearsonsRSquaredCalculator rSquaredCalculator = new OnlinePearsonsRSquaredCalculator();
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
+        double estimated = secondEnumerator.Current;
+        double original = firstEnumerator.Current;
+        rSquaredCalculator.Add(original, estimated);
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
+      } else {
+        errorState = rSquaredCalculator.ErrorState;
+        return rSquaredCalculator.RSquared;
+      }
+    }
+  }
+}
