Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/HeuristicLab.Problems.DataAnalysis.Tests-3.4.csproj
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/HeuristicLab.Problems.DataAnalysis.Tests-3.4.csproj	(revision 5951)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/HeuristicLab.Problems.DataAnalysis.Tests-3.4.csproj	(revision 5952)
@@ -109,4 +109,5 @@
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="OnlineCalculatorPerformanceTest.cs" />
     <Compile Include="TableFileParserTest.cs" />
     <Compile Include="StatisticCalculatorsTest.cs" />
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/OnlineCalculatorPerformanceTest.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/OnlineCalculatorPerformanceTest.cs	(revision 5952)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Tests/OnlineCalculatorPerformanceTest.cs	(revision 5952)
@@ -0,0 +1,103 @@
+﻿#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.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using HeuristicLab.Random;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+
+namespace HeuristicLab.Problems.DataAnalysis.Tests {
+  [TestClass]
+  public class OnlineCalculatorPerformanceTest {
+    private const int Rows = 5000;
+    private const int Columns = 2;
+    private const int Repetitions = 10000;
+
+    private TestContext testContextInstance;
+
+    /// <summary>
+    ///Gets or sets the test context which provides
+    ///information about and functionality for the current test run.
+    ///</summary>
+    public TestContext TestContext {
+      get { return testContextInstance; }
+      set { testContextInstance = value; }
+    }
+
+
+    [TestMethod]
+    public void OnlineAccuracyCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlineAccuracyCalculator.Calculate);
+    }
+    [TestMethod]
+    public void OnlineCovarianceCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlineCovarianceCalculator.Calculate);
+    }
+    [TestMethod]
+    public void OnlineMeanAbsolutePercentageErrorCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlineMeanAbsolutePercentageErrorCalculator.Calculate);
+    }
+
+    [TestMethod]
+    public void OnlineMeanSquaredErrorCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlineMeanSquaredErrorCalculator.Calculate);
+    }
+    [TestMethod]
+    public void OnlineNormalizedMeanSquaredErrorCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlineNormalizedMeanSquaredErrorCalculator.Calculate);
+    }
+    [TestMethod]
+    public void OnlinePearsonsRSquaredCalculatorPerformanceTest() {
+      TestCalculatorPerfomance(OnlinePearsonsRSquaredCalculator.Calculate);
+    }
+
+    private delegate double CalcateFunc(IEnumerable<double> estimated, IEnumerable<double> original, out OnlineCalculatorError errorState);
+    private void TestCalculatorPerfomance(CalcateFunc calculateFunc) {
+      var twister = new MersenneTwister(31415);
+      var dataset = CreateRandomDataset(twister, Rows, Columns);
+      var errorState = new OnlineCalculatorError();
+
+      Stopwatch watch = new Stopwatch();
+      watch.Start();
+      for (int i = 0; i < Repetitions; i++) {
+        double value = calculateFunc(dataset.GetEnumeratedVariableValues(0), dataset.GetEnumeratedVariableValues(1), out errorState);
+
+      }
+      watch.Stop();
+
+      TestContext.WriteLine("");
+      TestContext.WriteLine("Calculated Rows per milisecond: {0}.", Rows * Repetitions * 1.0 / watch.ElapsedMilliseconds);
+    }
+
+    public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) {
+      double[,] data = new double[rows, columns];
+      for (int i = 0; i < rows; i++) {
+        for (int j = 0; j < columns; j++) {
+          data[i, j] = twister.NextDouble() * 2.0 - 1.0;
+        }
+      }
+      IEnumerable<string> variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()));
+      Dataset ds = new Dataset(variableNames, data);
+      return ds;
+    }
+  }
+}
