Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceNNOne.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceNNOne.cs	(revision 8765)
+++ 	(revision )
@@ -1,128 +1,0 @@
-﻿using System;
-using System.Collections.Generic;
-using System.Linq;
-
-namespace HeuristicLab.Algorithms.DataAnalysis.GaussianProcess {
-  public class CovarianceNNOne : ICovarianceFunction {
-    private double[,] x;
-    private double[,] xt;
-    private double sf2;
-    private double l2;
-    private double[,] S;
-    private double[] sx;
-    private double[] sz;
-    private double sxsx;
-    private double sxsz;
-
-    public int NumberOfParameters {
-      get { return 2; }
-    }
-
-    public void SetMatrix(double[,] x) {
-      SetMatrix(x, x);
-    }
-
-    public void SetMatrix(double[,] x, double[,] xt) {
-      this.x = x;
-      this.xt = xt;
-      S = null;
-      sx = null;
-      sz = null;
-    }
-
-    public void SetHyperparamter(double[] hyp) {
-      if (hyp.Length != 2) throw new ArgumentException();
-      this.l2 = Math.Exp(2 * hyp[0]);
-      this.sf2 = Math.Exp(2 * hyp[1]);
-      S = null;
-      sx = null;
-      sz = null;
-    }
-
-    public double GetCovariance(int i, int j) {
-      if (S == null) CalculateVectorProducts();
-      if (sx == null) CalculateSx();
-      bool symmetric = x == xt;
-      double k;
-      if (symmetric) {
-        k = S[i, j] / sxsx;
-      } else {
-        k = S[i, j] / sxsz;
-      }
-      return sf2 * Math.Asin(k);
-    }
-
-
-    public double[] GetDiagonalCovariances() {
-      if (x != xt) throw new InvalidOperationException();
-      if (sx == null) CalculateSx();
-      int rows = x.GetLength(0);
-      var k = new double[rows];
-      for (int i = 0; i < rows; i++) {
-        k[i] = sx[i] / (sx[i] + l2);
-        k[i] = sf2 * Math.Asin(k[i]);
-      }
-      return k;
-    }
-
-    public double[] GetDerivatives(int i, int j) {
-      double[] dhyp = new double[NumberOfParameters];
-      double[] vx = sx.Select(e => e / (l2 + e) / 2).ToArray();
-
-      double k;
-      double v;
-      if (x == xt) {
-        k = S[i, j] / sxsx;
-        v = vx[i] + vx[j];
-      } else {
-        double[] vz = sz.Select(e => e / (l2 + e) / 2).ToArray();
-        v = vx[i] + vz[j];
-        k = S[i, j] / sxsz;
-      }
-      dhyp[0] = -2 * sf2 * (k - k * v) / Math.Sqrt(1 - k * k);
-      dhyp[1] = 2.0 * sf2 * Math.Asin(k);
-      return dhyp;
-    }
-
-    private void CalculateSx() {
-      this.sx = new double[x.GetLength(0)];
-      for (int i = 0; i < sx.Length; i++) {
-        sx[i] = 1 + Product(GetRow(x, i), GetRow(x, i));
-      }
-      this.sz = new double[xt.GetLength(0)];
-      for (int i = 0; i < sz.Length; i++) {
-        sz[i] = 1 + Product(GetRow(xt, i), GetRow(xt, i));
-      }
-
-      sxsx = Product(sx.Select(e => Math.Sqrt(l2 + e)), sx.Select(e => Math.Sqrt(l2 + e)));
-      sxsz = Product(sx.Select(e => Math.Sqrt(l2 + e)), sz.Select(e => Math.Sqrt(l2 + e)));
-    }
-
-    private void CalculateVectorProducts() {
-      if (x.GetLength(1) != xt.GetLength(1)) throw new InvalidOperationException();
-      int rows = x.GetLength(0);
-      int cols = xt.GetLength(0);
-      S = new double[rows, cols];
-      bool symmetric = x == xt;
-      for (int i = 0; i < rows; i++) {
-        for (int j = i; j < rows; j++) {
-          S[i, j] = 1 + Product(GetRow(x, i), GetRow(xt, j));
-          if (symmetric) {
-            S[j, i] = S[i, j];
-          } else {
-            S[j, i] = 1 + Product(GetRow(x, j), GetRow(xt, i));
-          }
-        }
-      }
-    }
-
-
-    private double Product(IEnumerable<double> x, IEnumerable<double> y) {
-      return x.Zip(y, (a, b) => a * b).Sum();
-    }
-    private static IEnumerable<double> GetRow(double[,] x, int r) {
-      int cols = x.GetLength(1);
-      return Enumerable.Range(0, cols).Select(c => x[r, c]);
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceProd.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceProd.cs	(revision 8765)
+++ 	(revision )
@@ -1,133 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2012 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 System.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "CovarianceProd",
-    Description = "Product covariance function for Gaussian processes.")]
-  public class CovarianceProd : Item, ICovarianceFunction {
-    [Storable]
-    private ItemList<ICovarianceFunction> factors;
-
-    [Storable]
-    private int numberOfVariables;
-    public ItemList<ICovarianceFunction> Factors {
-      get { return factors; }
-    }
-
-    [StorableConstructor]
-    protected CovarianceProd(bool deserializing)
-      : base(deserializing) {
-    }
-
-    protected CovarianceProd(CovarianceProd original, Cloner cloner)
-      : base(original, cloner) {
-      this.factors = cloner.Clone(original.factors);
-      this.numberOfVariables = original.numberOfVariables;
-      AttachEventHandlers();
-    }
-
-    public CovarianceProd()
-      : base() {
-      this.factors = new ItemList<ICovarianceFunction>();
-      AttachEventHandlers();
-    }
-
-    private void AttachEventHandlers() {
-      this.factors.CollectionReset += (sender, args) => ClearCache();
-      this.factors.ItemsAdded += (sender, args) => ClearCache();
-      this.factors.ItemsRemoved += (sender, args) => ClearCache();
-      this.factors.ItemsReplaced += (sender, args) => ClearCache();
-      this.factors.ItemsMoved += (sender, args) => ClearCache();
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new CovarianceProd(this, cloner);
-    }
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      this.numberOfVariables = numberOfVariables;
-      return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
-    }
-
-    public void SetParameter(double[] hyp) {
-      int offset = 0;
-      foreach (var t in factors) {
-        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
-        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
-        offset += numberOfParameters;
-      }
-    }
-    public void SetData(double[,] x) {
-      SetData(x, x);
-    }
-
-    public void SetData(double[,] x, double[,] xt) {
-      foreach (var t in factors) {
-        t.SetData(x, xt);
-      }
-    }
-
-    public double GetCovariance(int i, int j) {
-      return factors.Select(t => t.GetCovariance(i, j)).Aggregate((a, b) => a * b);
-    }
-
-    private Dictionary<int, Tuple<int, int>> cachedParameterMap;
-    public double GetGradient(int i, int j, int k) {
-      if (cachedParameterMap == null) {
-        CalculateParameterMap();
-      }
-      int ti = cachedParameterMap[k].Item1;
-      k = cachedParameterMap[k].Item2;
-      double res = 1.0;
-      for (int ii = 0; ii < factors.Count; ii++) {
-        var f = factors[ii];
-        if (ii == ti) {
-          res *= f.GetGradient(i, j, k);
-        } else {
-          res *= f.GetCovariance(i, j);
-        }
-      }
-      return res;
-    }
-
-    private void ClearCache() {
-      cachedParameterMap = null;
-    }
-
-    private void CalculateParameterMap() {
-      cachedParameterMap = new Dictionary<int, Tuple<int, int>>();
-      int k = 0;
-      for (int ti = 0; ti < factors.Count; ti++) {
-        for (int ti_k = 0; ti_k < factors[ti].GetNumberOfParameters(numberOfVariables); ti_k++) {
-          cachedParameterMap[k++] = Tuple.Create(ti, ti_k);
-        }
-      }
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceRQiso.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceRQiso.cs	(revision 8765)
+++ 	(revision )
@@ -1,145 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2012 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.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "CovarianceRQiso",
-    Description = "Isotropic rational quadratic covariance function for Gaussian processes.")]
-  public class CovarianceRQiso : Item, ICovarianceFunction {
-    [Storable]
-    private double[,] x;
-    [Storable]
-    private double[,] xt;
-    [Storable]
-    private double sf2;
-    public double Scale { get { return sf2; } }
-    [Storable]
-    private double l;
-    public double Length { get { return l; } }
-    [Storable]
-    private double alpha;
-    public double Shape { get { return alpha; } }
-    [Storable]
-    private bool symmetric;
-    private double[,] d2;
-
-    [StorableConstructor]
-    protected CovarianceRQiso(bool deserializing)
-      : base(deserializing) {
-    }
-
-    protected CovarianceRQiso(CovarianceRQiso original, Cloner cloner)
-      : base(original, cloner) {
-      if (original.x != null) {
-        this.x = new double[original.x.GetLength(0), original.x.GetLength(1)];
-        Array.Copy(original.x, this.x, x.Length);
-
-        this.xt = new double[original.xt.GetLength(0), original.xt.GetLength(1)];
-        Array.Copy(original.xt, this.xt, xt.Length);
-
-        this.d2 = new double[original.d2.GetLength(0), original.d2.GetLength(1)];
-        Array.Copy(original.d2, this.d2, d2.Length);
-        this.sf2 = original.sf2;
-      }
-      this.sf2 = original.sf2;
-      this.l = original.l;
-      this.alpha = original.alpha;
-      this.symmetric = original.symmetric;
-    }
-
-    public CovarianceRQiso()
-      : base() {
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new CovarianceRQiso(this, cloner);
-    }
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      return 3;
-    }
-
-    public void SetParameter(double[] hyp) {
-      this.l = Math.Exp(hyp[0]);
-      this.sf2 = Math.Exp(2 * hyp[1]);
-      this.alpha = Math.Exp(hyp[2]);
-      d2 = null;
-    }
-    public void SetData(double[,] x) {
-      SetData(x, x);
-      this.symmetric = true;
-    }
-
-
-    public void SetData(double[,] x, double[,] xt) {
-      this.symmetric = false;
-      this.x = x;
-      this.xt = xt;
-      d2 = null;
-    }
-
-    public double GetCovariance(int i, int j) {
-      if (d2 == null) CalculateSquaredDistances();
-      return sf2 * Math.Pow(1 + 0.5 * d2[i, j] / alpha, -alpha);
-    }
-
-    public double GetGradient(int i, int j, int k) {
-      switch (k) {
-        case 0: return sf2 * Math.Pow(1 + 0.5 * d2[i, j] / alpha, -alpha - 1) * d2[i, j];
-        case 1: return 2 * sf2 * Math.Pow((1 + 0.5 * d2[i, j] / alpha), (-alpha));
-        case 2: {
-            double g = (1 + 0.5 * d2[i, j] / alpha);
-            g = sf2 * Math.Pow(g, -alpha) * (0.5 * d2[i, j] / g - alpha * Math.Log(g));
-            return g;
-          }
-        default: throw new ArgumentException("CovarianceRQiso has three hyperparameters", "k");
-      }
-    }
-
-    private void CalculateSquaredDistances() {
-      if (x.GetLength(1) != xt.GetLength(1)) throw new InvalidOperationException();
-      int rows = x.GetLength(0);
-      int cols = xt.GetLength(0);
-      d2 = new double[rows, cols];
-      double lInv = 1.0 / l;
-      if (symmetric) {
-        for (int i = 0; i < rows; i++) {
-          for (int j = i; j < rows; j++) {
-            d2[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
-            d2[j, i] = d2[i, j];
-          }
-        }
-      } else {
-        for (int i = 0; i < rows; i++) {
-          for (int j = 0; j < cols; j++) {
-            d2[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
-          }
-        }
-      }
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSEard.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSEard.cs	(revision 8765)
+++ 	(revision )
@@ -1,146 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2012 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.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "CovarianceSEard", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]
-  public class CovarianceSEard : Item, ICovarianceFunction {
-    [Storable]
-    private double[,] x;
-    [Storable]
-    private double[,] xt;
-    [Storable]
-    private double sf2;
-    public double Scale { get { return sf2; } }
-
-    [Storable]
-    private double[] l;
-    public double[] Length {
-      get {
-        if (l == null) return new double[0];
-        var copy = new double[l.Length];
-        Array.Copy(l, copy, copy.Length);
-        return copy;
-      }
-    }
-
-    private double[,] sd;
-    private bool symmetric;
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      return numberOfVariables + 1;
-    }
-    [StorableConstructor]
-    protected CovarianceSEard(bool deserializing) : base(deserializing) { }
-    protected CovarianceSEard(CovarianceSEard original, Cloner cloner)
-      : base(original, cloner) {
-      if (original.x != null) {
-        this.x = new double[original.x.GetLength(0), original.x.GetLength(1)];
-        Array.Copy(original.x, this.x, x.Length);
-
-        this.xt = new double[original.xt.GetLength(0), original.xt.GetLength(1)];
-        Array.Copy(original.xt, this.xt, xt.Length);
-
-        this.sd = new double[original.sd.GetLength(0), original.sd.GetLength(1)];
-        Array.Copy(original.sd, this.sd, sd.Length);
-
-        this.l = new double[original.l.Length];
-        Array.Copy(original.l, this.l, l.Length);
-      }
-      this.sf2 = original.sf2;
-      this.symmetric = original.symmetric;
-    }
-    public CovarianceSEard()
-      : base() {
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new CovarianceSEard(this, cloner);
-    }
-
-    public void SetParameter(double[] hyp) {
-      this.l = hyp.Take(hyp.Length - 1).Select(Math.Exp).ToArray();
-      this.sf2 = Math.Exp(2 * hyp[hyp.Length - 1]);
-      // sf2 = Math.Min(10E6, sf2); // upper limit for the scale
-
-      sd = null;
-    }
-
-    public void SetData(double[,] x) {
-      SetData(x, x);
-      this.symmetric = true;
-    }
-
-    public void SetData(double[,] x, double[,] xt) {
-      this.x = x;
-      this.xt = xt;
-      this.symmetric = false;
-
-      sd = null;
-    }
-
-    public double GetCovariance(int i, int j) {
-      if (sd == null) CalculateSquaredDistances();
-      return sf2 * Math.Exp(-sd[i, j] / 2.0);
-    }
-
-    public double GetGradient(int i, int j, int k) {
-      if (k < l.Length) {
-        double sqrDist = Util.SqrDist(x[i, k] / l[k], xt[j, k] / l[k]);
-        return sf2 * Math.Exp(-sd[i, j] / 2.0) * sqrDist;
-      } else if (k == l.Length) {
-        return 2.0 * sf2 * Math.Exp(-sd[i, j] / 2.0);
-      } else {
-        throw new ArgumentException("CovarianceSEard has dimension+1 hyperparameters.", "k");
-      }
-    }
-
-
-    private void CalculateSquaredDistances() {
-      if (x.GetLength(1) != xt.GetLength(1)) throw new InvalidOperationException();
-      int rows = x.GetLength(0);
-      int cols = xt.GetLength(0);
-      sd = new double[rows, cols];
-      if (symmetric) {
-        for (int i = 0; i < rows; i++) {
-          for (int j = i; j < cols; j++) {
-            sd[i, j] = Util.SqrDist(Util.GetRow(x, i).Select((e, k) => e / l[k]),
-                                    Util.GetRow(xt, j).Select((e, k) => e / l[k]));
-            sd[j, i] = sd[i, j];
-          }
-        }
-      } else {
-        for (int i = 0; i < rows; i++) {
-          for (int j = 0; j < cols; j++) {
-            sd[i, j] = Util.SqrDist(Util.GetRow(x, i).Select((e, k) => e / l[k]),
-                                    Util.GetRow(xt, j).Select((e, k) => e / l[k]));
-          }
-        }
-      }
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSEiso.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceSEiso.cs	(revision 8765)
+++ 	(revision )
@@ -1,135 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2012 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.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "CovarianceSEiso",
-    Description = "Isotropic squared exponential covariance function for Gaussian processes.")]
-  public class CovarianceSEiso : Item, ICovarianceFunction {
-    [Storable]
-    private double[,] x;
-    [Storable]
-    private double[,] xt;
-    [Storable]
-    private double sf2;
-    public double Scale { get { return sf2; } }
-    [Storable]
-    private double l;
-    public double Length { get { return l; } }
-    [Storable]
-    private bool symmetric;
-    private double[,] sd;
-
-    [StorableConstructor]
-    protected CovarianceSEiso(bool deserializing)
-      : base(deserializing) {
-    }
-
-    protected CovarianceSEiso(CovarianceSEiso original, Cloner cloner)
-      : base(original, cloner) {
-      if (original.x != null) {
-        this.x = new double[original.x.GetLength(0), original.x.GetLength(1)];
-        Array.Copy(original.x, this.x, x.Length);
-
-        this.xt = new double[original.xt.GetLength(0), original.xt.GetLength(1)];
-        Array.Copy(original.xt, this.xt, xt.Length);
-
-        this.sd = new double[original.sd.GetLength(0), original.sd.GetLength(1)];
-        Array.Copy(original.sd, this.sd, sd.Length);
-        this.sf2 = original.sf2;
-      }
-      this.sf2 = original.sf2;
-      this.l = original.l;
-      this.symmetric = original.symmetric;
-    }
-
-    public CovarianceSEiso()
-      : base() {
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new CovarianceSEiso(this, cloner);
-    }
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      return 2;
-    }
-
-    public void SetParameter(double[] hyp) {
-      this.l = Math.Exp(hyp[0]);
-      this.sf2 = Math.Exp(2 * hyp[1]);
-      sd = null;
-    }
-    public void SetData(double[,] x) {
-      SetData(x, x);
-      this.symmetric = true;
-    }
-
-
-    public void SetData(double[,] x, double[,] xt) {
-      this.symmetric = false;
-      this.x = x;
-      this.xt = xt;
-      sd = null;
-    }
-
-    public double GetCovariance(int i, int j) {
-      if (sd == null) CalculateSquaredDistances();
-      return sf2 * Math.Exp(-sd[i, j] / 2.0);
-    }
-
-    public double GetGradient(int i, int j, int k) {
-      switch (k) {
-        case 0: return sf2 * Math.Exp(-sd[i, j] / 2.0) * sd[i, j];
-        case 1: return 2.0 * sf2 * Math.Exp(-sd[i, j] / 2.0);
-        default: throw new ArgumentException("CovarianceSEiso has two hyperparameters", "k");
-      }
-    }
-
-    private void CalculateSquaredDistances() {
-      if (x.GetLength(1) != xt.GetLength(1)) throw new InvalidOperationException();
-      int rows = x.GetLength(0);
-      int cols = xt.GetLength(0);
-      sd = new double[rows, cols];
-      double lInv = 1.0 / l;
-      if (symmetric) {
-        for (int i = 0; i < rows; i++) {
-          for (int j = i; j < rows; j++) {
-            sd[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
-            sd[j, i] = sd[i, j];
-          }
-        }
-      } else {
-        for (int i = 0; i < rows; i++) {
-          for (int j = 0; j < cols; j++) {
-            sd[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
-          }
-        }
-      }
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanProd.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanProd.cs	(revision 8765)
+++ 	(revision )
@@ -1,109 +1,0 @@
-﻿#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2012 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.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
-  [Item(Name = "MeanProd", Description = "Product of mean functions for Gaussian processes.")]
-  public class MeanProd : Item, IMeanFunction {
-    [Storable]
-    private ItemList<IMeanFunction> factors;
-
-    [Storable]
-    private int numberOfVariables;
-
-    public ItemList<IMeanFunction> Factors {
-      get { return factors; }
-    }
-
-    public int GetNumberOfParameters(int numberOfVariables) {
-      this.numberOfVariables = numberOfVariables;
-      return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
-    }
-
-    [StorableConstructor]
-    protected MeanProd(bool deserializing)
-      : base(deserializing) {
-    }
-
-    protected MeanProd(MeanProd original, Cloner cloner)
-      : base(original, cloner) {
-      this.factors = cloner.Clone(original.factors);
-      this.numberOfVariables = original.numberOfVariables;
-    }
-
-    public MeanProd() {
-      this.factors = new ItemList<IMeanFunction>();
-    }
-
-    public void SetParameter(double[] hyp) {
-      int offset = 0;
-      foreach (var t in factors) {
-        var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
-        t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
-        offset += numberOfParameters;
-      }
-    }
-
-    public void SetData(double[,] x) {
-      foreach (var t in factors) t.SetData(x);
-    }
-
-    public double[] GetMean(double[,] x) {
-      var res = factors.First().GetMean(x);
-      foreach (var t in factors.Skip(1)) {
-        var a = t.GetMean(x);
-        for (int i = 0; i < res.Length; i++) res[i] *= a[i];
-      }
-      return res;
-    }
-
-    public double[] GetGradients(int k, double[,] x) {
-      double[] res = Enumerable.Repeat(1.0, x.GetLength(0)).ToArray();
-      // find index of factor for the given k
-      int j = 0;
-      while (k >= factors[j].GetNumberOfParameters(numberOfVariables)) {
-        k -= factors[j].GetNumberOfParameters(numberOfVariables);
-        j++;
-      }
-      for (int i = 0; i < factors.Count; i++) {
-        var f = factors[i];
-        if (i == j) {
-          // multiply gradient
-          var g = f.GetGradients(k, x);
-          for (int ii = 0; ii < res.Length; ii++) res[ii] *= g[ii];
-        } else {
-          // multiply mean
-          var m = f.GetMean(x);
-          for (int ii = 0; ii < res.Length; ii++) res[ii] *= m[ii];
-        }
-      }
-      return res;
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new MeanProd(this, cloner);
-    }
-  }
-}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs	(revision 8766)
@@ -0,0 +1,275 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class AbstractFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.components = new System.ComponentModel.Container();
+      System.ComponentModel.ComponentResourceManager resources = new System.ComponentModel.ComponentResourceManager(typeof(AbstractFeatureCorrelationView));
+      this.DataGridView = new System.Windows.Forms.DataGridView();
+      this.HeatMapProgressBar = new System.Windows.Forms.ProgressBar();
+      this.PartitionComboBox = new System.Windows.Forms.ComboBox();
+      this.CorrelationCalcLabel = new System.Windows.Forms.Label();
+      this.CorrelationCalcComboBox = new System.Windows.Forms.ComboBox();
+      this.PartitionLabel = new System.Windows.Forms.Label();
+      this.minimumLabel = new System.Windows.Forms.Label();
+      this.maximumLabel = new System.Windows.Forms.Label();
+      this.PictureBox = new System.Windows.Forms.PictureBox();
+      this.SplitContainer = new System.Windows.Forms.SplitContainer();
+      this.CalculatingPanel = new System.Windows.Forms.Panel();
+      this.CalculatingLabel = new System.Windows.Forms.Label();
+      this.contextMenu = new System.Windows.Forms.ContextMenuStrip(this.components);
+      this.ShowHideColumns = new System.Windows.Forms.ToolStripMenuItem();
+      ((System.ComponentModel.ISupportInitialize)(this.DataGridView)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).BeginInit();
+      this.SplitContainer.Panel1.SuspendLayout();
+      this.SplitContainer.Panel2.SuspendLayout();
+      this.SplitContainer.SuspendLayout();
+      this.CalculatingPanel.SuspendLayout();
+      this.contextMenu.SuspendLayout();
+      this.SuspendLayout();
+      // 
+      // DataGridView
+      // 
+      this.DataGridView.AllowUserToAddRows = false;
+      this.DataGridView.AllowUserToDeleteRows = false;
+      this.DataGridView.ColumnHeadersHeightSizeMode = System.Windows.Forms.DataGridViewColumnHeadersHeightSizeMode.AutoSize;
+      this.DataGridView.Dock = System.Windows.Forms.DockStyle.Fill;
+      this.DataGridView.Location = new System.Drawing.Point(0, 0);
+      this.DataGridView.Name = "DataGridView";
+      this.DataGridView.ReadOnly = true;
+      this.DataGridView.Size = new System.Drawing.Size(475, 301);
+      this.DataGridView.TabIndex = 0;
+      this.DataGridView.VirtualMode = true;
+      this.DataGridView.CellPainting += new System.Windows.Forms.DataGridViewCellPaintingEventHandler(this.DataGridView_CellPainting);
+      this.DataGridView.CellValueNeeded += new System.Windows.Forms.DataGridViewCellValueEventHandler(this.DataGridView_CellValueNeeded);
+      this.DataGridView.ColumnHeaderMouseClick += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.DataGridView_ColumnHeaderMouseClick);
+      this.DataGridView.KeyDown += new System.Windows.Forms.KeyEventHandler(this.DataGridView_KeyDown);
+      this.DataGridView.MouseClick += new System.Windows.Forms.MouseEventHandler(this.DataGridView_MouseClick);
+      // 
+      // HeatMapProgressBar
+      // 
+      this.HeatMapProgressBar.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Left | System.Windows.Forms.AnchorStyles.Right)));
+      this.HeatMapProgressBar.Location = new System.Drawing.Point(25, 46);
+      this.HeatMapProgressBar.Name = "HeatMapProgressBar";
+      this.HeatMapProgressBar.RightToLeft = System.Windows.Forms.RightToLeft.No;
+      this.HeatMapProgressBar.Size = new System.Drawing.Size(154, 21);
+      this.HeatMapProgressBar.TabIndex = 9;
+      // 
+      // PartitionComboBox
+      // 
+      this.PartitionComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.PartitionComboBox.FormattingEnabled = true;
+      this.PartitionComboBox.Location = new System.Drawing.Point(333, 3);
+      this.PartitionComboBox.Name = "PartitionComboBox";
+      this.PartitionComboBox.Size = new System.Drawing.Size(142, 21);
+      this.PartitionComboBox.TabIndex = 8;
+      this.PartitionComboBox.SelectionChangeCommitted += new System.EventHandler(this.PartitionComboBox_SelectedChangeCommitted);
+      // 
+      // CorrelationCalcLabel
+      // 
+      this.CorrelationCalcLabel.AutoSize = true;
+      this.CorrelationCalcLabel.Location = new System.Drawing.Point(0, 6);
+      this.CorrelationCalcLabel.Name = "CorrelationCalcLabel";
+      this.CorrelationCalcLabel.Size = new System.Drawing.Size(104, 13);
+      this.CorrelationCalcLabel.TabIndex = 7;
+      this.CorrelationCalcLabel.Text = "Correlation Measure:";
+      // 
+      // CorrelationCalcComboBox
+      // 
+      this.CorrelationCalcComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.CorrelationCalcComboBox.FormattingEnabled = true;
+      this.CorrelationCalcComboBox.Location = new System.Drawing.Point(110, 3);
+      this.CorrelationCalcComboBox.Name = "CorrelationCalcComboBox";
+      this.CorrelationCalcComboBox.Size = new System.Drawing.Size(163, 21);
+      this.CorrelationCalcComboBox.TabIndex = 6;
+      this.CorrelationCalcComboBox.SelectionChangeCommitted += new System.EventHandler(this.CorrelationMeasureComboBox_SelectedChangeCommitted);
+      // 
+      // PartitionLabel
+      // 
+      this.PartitionLabel.AutoSize = true;
+      this.PartitionLabel.Location = new System.Drawing.Point(279, 6);
+      this.PartitionLabel.Name = "PartitionLabel";
+      this.PartitionLabel.Size = new System.Drawing.Size(48, 13);
+      this.PartitionLabel.TabIndex = 10;
+      this.PartitionLabel.Text = "Partition:";
+      // 
+      // minimumLabel
+      // 
+      this.minimumLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
+      this.minimumLabel.BackColor = System.Drawing.Color.Transparent;
+      this.minimumLabel.Location = new System.Drawing.Point(487, 314);
+      this.minimumLabel.Name = "minimumLabel";
+      this.minimumLabel.Size = new System.Drawing.Size(73, 19);
+      this.minimumLabel.TabIndex = 13;
+      this.minimumLabel.Text = "0.0";
+      this.minimumLabel.TextAlign = System.Drawing.ContentAlignment.TopCenter;
+      // 
+      // maximumLabel
+      // 
+      this.maximumLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
+      this.maximumLabel.BackColor = System.Drawing.Color.Transparent;
+      this.maximumLabel.Location = new System.Drawing.Point(487, 2);
+      this.maximumLabel.Name = "maximumLabel";
+      this.maximumLabel.Size = new System.Drawing.Size(73, 25);
+      this.maximumLabel.TabIndex = 12;
+      this.maximumLabel.Text = "1.0";
+      this.maximumLabel.TextAlign = System.Drawing.ContentAlignment.BottomCenter;
+      // 
+      // PictureBox
+      // 
+      this.PictureBox.Anchor = ((System.Windows.Forms.AnchorStyles)(((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.PictureBox.BorderStyle = System.Windows.Forms.BorderStyle.FixedSingle;
+      this.PictureBox.Image = ((System.Drawing.Image)(resources.GetObject("PictureBox.Image")));
+      this.PictureBox.Location = new System.Drawing.Point(507, 30);
+      this.PictureBox.Name = "PictureBox";
+      this.PictureBox.Size = new System.Drawing.Size(35, 281);
+      this.PictureBox.SizeMode = System.Windows.Forms.PictureBoxSizeMode.StretchImage;
+      this.PictureBox.TabIndex = 15;
+      this.PictureBox.TabStop = false;
+      // 
+      // SplitContainer
+      // 
+      this.SplitContainer.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Left)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.SplitContainer.FixedPanel = System.Windows.Forms.FixedPanel.Panel1;
+      this.SplitContainer.IsSplitterFixed = true;
+      this.SplitContainer.Location = new System.Drawing.Point(3, 3);
+      this.SplitContainer.Name = "SplitContainer";
+      this.SplitContainer.Orientation = System.Windows.Forms.Orientation.Horizontal;
+      // 
+      // SplitContainer.Panel1
+      // 
+      this.SplitContainer.Panel1.Controls.Add(this.CorrelationCalcLabel);
+      this.SplitContainer.Panel1.Controls.Add(this.CorrelationCalcComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.PartitionComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.PartitionLabel);
+      // 
+      // SplitContainer.Panel2
+      // 
+      this.SplitContainer.Panel2.Controls.Add(this.CalculatingPanel);
+      this.SplitContainer.Panel2.Controls.Add(this.DataGridView);
+      this.SplitContainer.Size = new System.Drawing.Size(475, 330);
+      this.SplitContainer.SplitterDistance = 25;
+      this.SplitContainer.TabIndex = 16;
+      // 
+      // CalculatingPanel
+      // 
+      this.CalculatingPanel.Anchor = System.Windows.Forms.AnchorStyles.None;
+      this.CalculatingPanel.Controls.Add(this.CalculatingLabel);
+      this.CalculatingPanel.Controls.Add(this.HeatMapProgressBar);
+      this.CalculatingPanel.Location = new System.Drawing.Point(138, 95);
+      this.CalculatingPanel.Name = "CalculatingPanel";
+      this.CalculatingPanel.Size = new System.Drawing.Size(200, 81);
+      this.CalculatingPanel.TabIndex = 10;
+      // 
+      // CalculatingLabel
+      // 
+      this.CalculatingLabel.AutoSize = true;
+      this.CalculatingLabel.Location = new System.Drawing.Point(42, 19);
+      this.CalculatingLabel.Name = "CalculatingLabel";
+      this.CalculatingLabel.Size = new System.Drawing.Size(120, 13);
+      this.CalculatingLabel.TabIndex = 10;
+      this.CalculatingLabel.Text = "Calculating correlation...";
+      // 
+      // contextMenu
+      // 
+      this.contextMenu.Items.AddRange(new System.Windows.Forms.ToolStripItem[] {
+            this.ShowHideColumns});
+      this.contextMenu.Name = "contextMenu";
+      this.contextMenu.Size = new System.Drawing.Size(191, 26);
+      // 
+      // ShowHideColumns
+      // 
+      this.ShowHideColumns.Name = "ShowHideColumns";
+      this.ShowHideColumns.Size = new System.Drawing.Size(190, 22);
+      this.ShowHideColumns.Text = "Show / Hide Columns";
+      this.ShowHideColumns.Click += new System.EventHandler(this.ShowHideColumns_Click);
+      // 
+      // AbstractFeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Controls.Add(this.SplitContainer);
+      this.Controls.Add(this.minimumLabel);
+      this.Controls.Add(this.PictureBox);
+      this.Controls.Add(this.maximumLabel);
+      this.Name = "AbstractFeatureCorrelationView";
+      this.Size = new System.Drawing.Size(569, 336);
+      ((System.ComponentModel.ISupportInitialize)(this.DataGridView)).EndInit();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).EndInit();
+      this.SplitContainer.Panel1.ResumeLayout(false);
+      this.SplitContainer.Panel1.PerformLayout();
+      this.SplitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).EndInit();
+      this.SplitContainer.ResumeLayout(false);
+      this.CalculatingPanel.ResumeLayout(false);
+      this.CalculatingPanel.PerformLayout();
+      this.contextMenu.ResumeLayout(false);
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+
+    protected System.Windows.Forms.DataGridView DataGridView;
+    protected System.Windows.Forms.ProgressBar HeatMapProgressBar;
+    protected System.Windows.Forms.ComboBox PartitionComboBox;
+    protected System.Windows.Forms.Label CorrelationCalcLabel;
+    protected System.Windows.Forms.ComboBox CorrelationCalcComboBox;
+    protected System.Windows.Forms.Label PartitionLabel;
+    protected System.Windows.Forms.Label minimumLabel;
+    protected System.Windows.Forms.Label maximumLabel;
+    protected System.Windows.Forms.PictureBox PictureBox;
+    protected System.Windows.Forms.SplitContainer SplitContainer;
+    protected System.Windows.Forms.Panel CalculatingPanel;
+    protected System.Windows.Forms.Label CalculatingLabel;
+    protected System.Windows.Forms.ContextMenuStrip contextMenu;
+    protected System.Windows.Forms.ToolStripMenuItem ShowHideColumns;
+
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs	(revision 8766)
@@ -0,0 +1,348 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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 System.ComponentModel;
+using System.Drawing;
+using System.Linq;
+using System.Text;
+using System.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Common;
+using HeuristicLab.Data.Views;
+using HeuristicLab.DataAnalysis.Views;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public abstract partial class AbstractFeatureCorrelationView : AsynchronousContentView {
+
+    private int[] virtualRowIndices;
+    private VariableVisibilityDialog variableVisibility;
+    private List<KeyValuePair<int, SortOrder>> sortedColumnIndices;
+    private StringConvertibleMatrixView.RowComparer rowComparer;
+
+    protected FeatureCorrelationCalculator fcc;
+    protected HeatMap currentCorrelation;
+
+    public new DataAnalysisProblemData Content {
+      get { return (DataAnalysisProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    protected AbstractFeatureCorrelationView() {
+      InitializeComponent();
+      sortedColumnIndices = new List<KeyValuePair<int, SortOrder>>();
+      rowComparer = new StringConvertibleMatrixView.RowComparer();
+      fcc = new FeatureCorrelationCalculator();
+      var calculatorList = FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>().Select(x => new KeyValuePair<FeatureCorrelationEnums.CorrelationCalculators, string>(x, FeatureCorrelationEnums.GetEnumDescription(x))).ToList();
+      CorrelationCalcComboBox.ValueMember = "Key";
+      CorrelationCalcComboBox.DisplayMember = "Value";
+      CorrelationCalcComboBox.DataSource = new BindingList<KeyValuePair<FeatureCorrelationEnums.CorrelationCalculators, string>>(calculatorList);
+      var partitionList = FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.Partitions>().Select(x => new KeyValuePair<FeatureCorrelationEnums.Partitions, string>(x, FeatureCorrelationEnums.GetEnumDescription(x))).ToList();
+      PartitionComboBox.ValueMember = "Key";
+      PartitionComboBox.DisplayMember = "Value";
+      PartitionComboBox.DataSource = new BindingList<KeyValuePair<FeatureCorrelationEnums.Partitions, string>>(partitionList);
+    }
+
+    protected override void RegisterContentEvents() {
+      base.RegisterContentEvents();
+      fcc.ProgressCalculation += new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
+      fcc.CorrelationCalculationFinished += new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
+    }
+
+    protected override void DeregisterContentEvents() {
+      fcc.CorrelationCalculationFinished += new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
+      fcc.ProgressCalculation += new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
+      base.DeregisterContentEvents();
+    }
+
+    protected override void OnContentChanged() {
+      base.OnContentChanged();
+      if (Content != null) {
+        fcc.ProblemData = Content;
+        bool[] initialVisibility = SetInitialVisibilityOfColumns();
+
+        variableVisibility = new VariableVisibilityDialog(Content.Dataset.DoubleVariables, initialVisibility);
+        variableVisibility.VariableVisibilityChanged += new ItemCheckEventHandler(variableVisibility_VariableVisibilityChanged);
+        CalculateCorrelation();
+      } else {
+        DataGridView.Columns.Clear();
+        DataGridView.Rows.Clear();
+      }
+    }
+
+    protected virtual bool[] SetInitialVisibilityOfColumns() {
+      bool[] initialVisibility = new bool[Content.Dataset.DoubleVariables.Count()];
+      int i = 0;
+      foreach (var variable in Content.Dataset.DoubleVariables) {
+        initialVisibility[i] = Content.AllowedInputVariables.Contains(variable);
+        i++;
+      }
+      return initialVisibility;
+    }
+
+    protected abstract void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e);
+
+    protected void CorrelationMeasureComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
+      CalculateCorrelation();
+    }
+    protected void PartitionComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
+      CalculateCorrelation();
+    }
+
+    protected abstract void CalculateCorrelation();
+    protected abstract void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e);
+
+    protected void UpdateDataGrid() {
+      virtualRowIndices = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      DataGridViewColumn[] columns = new DataGridViewColumn[currentCorrelation.Columns];
+      for (int i = 0; i < columns.Length; ++i) {
+        var column = new DataGridViewTextBoxColumn();
+        column.FillWeight = 1;
+        columns[i] = column;
+      }
+
+      DataGridView.Columns.Clear();
+      DataGridView.Columns.AddRange(columns);
+
+      DataGridView.RowCount = currentCorrelation.Rows;
+
+      ClearSorting();
+      UpdateColumnHeaders();
+      UpdateRowHeaders();
+
+      maximumLabel.Text = currentCorrelation.Maximum.ToString();
+      minimumLabel.Text = currentCorrelation.Minimum.ToString();
+
+      DataGridView.AutoResizeColumns(DataGridViewAutoSizeColumnsMode.ColumnHeader);
+      DataGridView.AutoResizeRowHeadersWidth(DataGridViewRowHeadersWidthSizeMode.AutoSizeToDisplayedHeaders);
+      DataGridView.Enabled = true;
+    }
+
+    protected virtual void UpdateColumnHeaders() {
+      for (int i = 0; i < DataGridView.ColumnCount; i++) {
+        DataGridView.Columns[i].HeaderText = currentCorrelation.ColumnNames.ElementAt(i);
+        DataGridView.Columns[i].Visible = variableVisibility.Visibility[i];
+      }
+    }
+    protected virtual void UpdateRowHeaders() {
+      for (int i = 0; i < DataGridView.RowCount; i++) {
+        DataGridView.Rows[i].HeaderCell.Value = currentCorrelation.RowNames.ElementAt(virtualRowIndices[i]);
+        DataGridView.Rows[i].Visible = variableVisibility.Visibility[virtualRowIndices[i]];
+      }
+    }
+
+    protected void Content_ProgressCalculation(object sender, ProgressChangedEventArgs e) {
+      if (!CalculatingPanel.Visible && e.ProgressPercentage != HeatMapProgressBar.Maximum) {
+        CalculatingPanel.Show();
+      } else if (e.ProgressPercentage == HeatMapProgressBar.Maximum) {
+        CalculatingPanel.Hide();
+      }
+      HeatMapProgressBar.Value = e.ProgressPercentage;
+    }
+
+    protected void DataGridView_CellValueNeeded(object sender, DataGridViewCellValueEventArgs e) {
+      if (Content == null) return;
+      int rowIndex = virtualRowIndices[e.RowIndex];
+      e.Value = currentCorrelation[rowIndex, e.ColumnIndex];
+    }
+
+    protected void DataGridView_CellPainting(object sender, DataGridViewCellPaintingEventArgs e) {
+      if (Content == null) return;
+      if (e.RowIndex < 0) return;
+      if (e.ColumnIndex < 0) return;
+      if (e.State.HasFlag(DataGridViewElementStates.Selected)) return;
+      if (!e.PaintParts.HasFlag(DataGridViewPaintParts.Background)) return;
+
+      int rowIndex = virtualRowIndices[e.RowIndex];
+      Color backColor = GetDataPointColor(currentCorrelation[rowIndex, e.ColumnIndex], currentCorrelation.Minimum, currentCorrelation.Maximum);
+      using (Brush backColorBrush = new SolidBrush(backColor)) {
+        e.Graphics.FillRectangle(backColorBrush, e.CellBounds);
+      }
+      e.PaintContent(e.CellBounds);
+      e.Handled = true;
+    }
+
+    protected virtual Color GetDataPointColor(double value, double min, double max) {
+      if (double.IsNaN(value)) {
+        return Color.DarkGray;
+      }
+      IList<Color> colors = ColorGradient.Colors;
+      int index = (int)((colors.Count - 1) * (value - min) / (max - min));
+      if (index >= colors.Count) index = colors.Count - 1;
+      if (index < 0) index = 0;
+      return colors[index];
+    }
+
+    #region sort
+    protected void DataGridView_ColumnHeaderMouseClick(object sender, DataGridViewCellMouseEventArgs e) {
+      if (Content != null) {
+        if (e.Button == MouseButtons.Left) {
+          bool addToSortedIndices = (Control.ModifierKeys & Keys.Control) == Keys.Control;
+          SortOrder newSortOrder = SortOrder.Ascending;
+          if (sortedColumnIndices.Any(x => x.Key == e.ColumnIndex)) {
+            SortOrder oldSortOrder = sortedColumnIndices.Where(x => x.Key == e.ColumnIndex).First().Value;
+            int enumLength = Enum.GetValues(typeof(SortOrder)).Length;
+            newSortOrder = oldSortOrder = (SortOrder)Enum.Parse(typeof(SortOrder), ((((int)oldSortOrder) + 1) % enumLength).ToString());
+          }
+
+          if (!addToSortedIndices)
+            sortedColumnIndices.Clear();
+
+          if (sortedColumnIndices.Any(x => x.Key == e.ColumnIndex)) {
+            int sortedIndex = sortedColumnIndices.FindIndex(x => x.Key == e.ColumnIndex);
+            if (newSortOrder != SortOrder.None)
+              sortedColumnIndices[sortedIndex] = new KeyValuePair<int, SortOrder>(e.ColumnIndex, newSortOrder);
+            else
+              sortedColumnIndices.RemoveAt(sortedIndex);
+          } else
+            if (newSortOrder != SortOrder.None)
+              sortedColumnIndices.Add(new KeyValuePair<int, SortOrder>(e.ColumnIndex, newSortOrder));
+          Sort();
+        }
+      }
+    }
+
+    protected virtual void ClearSorting() {
+      virtualRowIndices = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      sortedColumnIndices.Clear();
+      UpdateSortGlyph();
+    }
+
+    private void Sort() {
+      virtualRowIndices = Sort(sortedColumnIndices);
+      UpdateSortGlyph();
+      UpdateRowHeaders();
+      DataGridView.Invalidate();
+    }
+
+    protected virtual int[] Sort(IEnumerable<KeyValuePair<int, SortOrder>> sortedColumns) {
+      int[] newSortedIndex = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      if (sortedColumns.Count() != 0) {
+        rowComparer.SortedIndices = sortedColumns;
+        rowComparer.Matrix = currentCorrelation;
+        Array.Sort(newSortedIndex, rowComparer);
+      }
+      return newSortedIndex;
+    }
+    private void UpdateSortGlyph() {
+      foreach (DataGridViewColumn col in this.DataGridView.Columns)
+        col.HeaderCell.SortGlyphDirection = SortOrder.None;
+      foreach (KeyValuePair<int, SortOrder> p in sortedColumnIndices)
+        this.DataGridView.Columns[p.Key].HeaderCell.SortGlyphDirection = p.Value;
+    }
+    #endregion
+
+    #region copy
+    protected void DataGridView_KeyDown(object sender, KeyEventArgs e) {
+      if (e.Control && e.KeyCode == Keys.C)
+        CopyValuesFromDataGridView();
+    }
+
+    private void CopyValuesFromDataGridView() {
+      if (DataGridView.SelectedCells.Count == 0) return;
+      StringBuilder s = new StringBuilder();
+      int minRowIndex = DataGridView.SelectedCells[0].RowIndex;
+      int maxRowIndex = DataGridView.SelectedCells[DataGridView.SelectedCells.Count - 1].RowIndex;
+      int minColIndex = DataGridView.SelectedCells[0].ColumnIndex;
+      int maxColIndex = DataGridView.SelectedCells[DataGridView.SelectedCells.Count - 1].ColumnIndex;
+
+      if (minRowIndex > maxRowIndex) {
+        int temp = minRowIndex;
+        minRowIndex = maxRowIndex;
+        maxRowIndex = temp;
+      }
+      if (minColIndex > maxColIndex) {
+        int temp = minColIndex;
+        minColIndex = maxColIndex;
+        maxColIndex = temp;
+      }
+
+      bool addRowNames = DataGridView.AreAllCellsSelected(false) && currentCorrelation.RowNames.Count() > 0;
+      bool addColumnNames = DataGridView.AreAllCellsSelected(false) && currentCorrelation.ColumnNames.Count() > 0;
+
+      //add colum names
+      if (addColumnNames) {
+        if (addRowNames)
+          s.Append('\t');
+
+        DataGridViewColumn column = DataGridView.Columns.GetFirstColumn(DataGridViewElementStates.Visible);
+        while (column != null) {
+          s.Append(column.HeaderText);
+          s.Append('\t');
+          column = DataGridView.Columns.GetNextColumn(column, DataGridViewElementStates.Visible, DataGridViewElementStates.None);
+        }
+        s.Remove(s.Length - 1, 1); //remove last tab
+        s.Append(Environment.NewLine);
+      }
+
+      for (int i = minRowIndex; i <= maxRowIndex; i++) {
+        int rowIndex = this.virtualRowIndices[i];
+        if (addRowNames) {
+          s.Append(currentCorrelation.RowNames.ElementAt(rowIndex));
+          s.Append('\t');
+        }
+
+        DataGridViewColumn column = DataGridView.Columns.GetFirstColumn(DataGridViewElementStates.Visible);
+        while (column != null) {
+          DataGridViewCell cell = DataGridView[column.Index, i];
+          if (cell.Selected) {
+            s.Append(currentCorrelation[rowIndex, column.Index]);
+            s.Append('\t');
+          }
+
+          column = DataGridView.Columns.GetNextColumn(column, DataGridViewElementStates.Visible, DataGridViewElementStates.None);
+        }
+        s.Remove(s.Length - 1, 1); //remove last tab
+        s.Append(Environment.NewLine);
+      }
+      Clipboard.SetText(s.ToString());
+    }
+    #endregion
+
+    protected void ShowHideColumns_Click(object sender, EventArgs e) {
+      variableVisibility.ShowDialog();
+    }
+
+    protected void DataGridView_MouseClick(object sender, MouseEventArgs e) {
+      if (Content == null) return;
+      if (e.Button == MouseButtons.Right && DataGridView.Columns.Count != 0)
+        contextMenu.Show(MousePosition);
+    }
+
+    protected int GetRowIndexOfVirtualindex(int virtualIndex) {
+      if (virtualIndex < 0 || virtualIndex >= virtualRowIndices.Length) {
+        throw new ArgumentException("Virtual index is out of bounds");
+      }
+
+      for (int i = 0; i < virtualRowIndices.Length; i++) {
+        if (virtualRowIndices[i] == virtualIndex) {
+          return i;
+        }
+      }
+      throw new ArgumentException("Virtual index was not found!");
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx	(revision 8766)
@@ -0,0 +1,1025 @@
+﻿<?xml version="1.0" encoding="utf-8"?>
+<root>
+  <!-- 
+    Microsoft ResX Schema 
+    
+    Version 2.0
+    
+    The primary goals of this format is to allow a simple XML format 
+    that is mostly human readable. The generation and parsing of the 
+    various data types are done through the TypeConverter classes 
+    associated with the data types.
+    
+    Example:
+    
+    ... ado.net/XML headers & schema ...
+    <resheader name="resmimetype">text/microsoft-resx</resheader>
+    <resheader name="version">2.0</resheader>
+    <resheader name="reader">System.Resources.ResXResourceReader, System.Windows.Forms, ...</resheader>
+    <resheader name="writer">System.Resources.ResXResourceWriter, System.Windows.Forms, ...</resheader>
+    <data name="Name1"><value>this is my long string</value><comment>this is a comment</comment></data>
+    <data name="Color1" type="System.Drawing.Color, System.Drawing">Blue</data>
+    <data name="Bitmap1" mimetype="application/x-microsoft.net.object.binary.base64">
+        <value>[base64 mime encoded serialized .NET Framework object]</value>
+    </data>
+    <data name="Icon1" type="System.Drawing.Icon, System.Drawing" mimetype="application/x-microsoft.net.object.bytearray.base64">
+        <value>[base64 mime encoded string representing a byte array form of the .NET Framework object]</value>
+        <comment>This is a comment</comment>
+    </data>
+                
+    There are any number of "resheader" rows that contain simple 
+    name/value pairs.
+    
+    Each data row contains a name, and value. The row also contains a 
+    type or mimetype. Type corresponds to a .NET class that support 
+    text/value conversion through the TypeConverter architecture. 
+    Classes that don't support this are serialized and stored with the 
+    mimetype set.
+    
+    The mimetype is used for serialized objects, and tells the 
+    ResXResourceReader how to depersist the object. This is currently not 
+    extensible. For a given mimetype the value must be set accordingly:
+    
+    Note - application/x-microsoft.net.object.binary.base64 is the format 
+    that the ResXResourceWriter will generate, however the reader can 
+    read any of the formats listed below.
+    
+    mimetype: application/x-microsoft.net.object.binary.base64
+    value   : The object must be serialized with 
+            : System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
+            : and then encoded with base64 encoding.
+    
+    mimetype: application/x-microsoft.net.object.soap.base64
+    value   : The object must be serialized with 
+            : System.Runtime.Serialization.Formatters.Soap.SoapFormatter
+            : and then encoded with base64 encoding.
+
+    mimetype: application/x-microsoft.net.object.bytearray.base64
+    value   : The object must be serialized into a byte array 
+            : using a System.ComponentModel.TypeConverter
+            : and then encoded with base64 encoding.
+    -->
+  <xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
+    <xsd:import namespace="http://www.w3.org/XML/1998/namespace" />
+    <xsd:element name="root" msdata:IsDataSet="true">
+      <xsd:complexType>
+        <xsd:choice maxOccurs="unbounded">
+          <xsd:element name="metadata">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" />
+              </xsd:sequence>
+              <xsd:attribute name="name" use="required" type="xsd:string" />
+              <xsd:attribute name="type" type="xsd:string" />
+              <xsd:attribute name="mimetype" type="xsd:string" />
+              <xsd:attribute ref="xml:space" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="assembly">
+            <xsd:complexType>
+              <xsd:attribute name="alias" type="xsd:string" />
+              <xsd:attribute name="name" type="xsd:string" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="data">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
+                <xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
+              </xsd:sequence>
+              <xsd:attribute name="name" type="xsd:string" use="required" msdata:Ordinal="1" />
+              <xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
+              <xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
+              <xsd:attribute ref="xml:space" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="resheader">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
+              </xsd:sequence>
+              <xsd:attribute name="name" type="xsd:string" use="required" />
+            </xsd:complexType>
+          </xsd:element>
+        </xsd:choice>
+      </xsd:complexType>
+    </xsd:element>
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+  <resheader name="resmimetype">
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+  <resheader name="version">
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+        /wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY/wAY
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+        Ff8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8A
+        Ff8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AFf8AABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/
+        ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/
+        ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/ABL/AAAP
+        /wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP/wAP
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+        AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAn/
+        AAn/AAn/AAn/AAn/AAn/AAn/AAn/AAAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG
+        /wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG/wAG
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+        A/8AA/8AA/8AA/8AA/8AA/8A
+</value>
+  </data>
+  <metadata name="contextMenu.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+    <value>17, 17</value>
+  </metadata>
+</root>
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs	(revision 8766)
@@ -0,0 +1,58 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  internal class FeatureCorrelationCache : Object {
+    private Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, double[,]>> correlationsCache;
+
+    public FeatureCorrelationCache()
+      : base() {
+      InitializeCaches();
+    }
+
+    private void InitializeCaches() {
+      correlationsCache = new Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, double[,]>>();
+      foreach (var calc in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>()) {
+        correlationsCache.Add(calc, new Dictionary<FeatureCorrelationEnums.Partitions, double[,]>());
+      }
+    }
+
+    public void Reset() {
+      InitializeCaches();
+    }
+
+    public double[,] GetCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition) {
+      double[,] corr;
+      correlationsCache[calc].TryGetValue(partition, out corr);
+      return corr;
+    }
+
+    public void SetCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, double[,] correlation) {
+      correlationsCache[calc][partition] = correlation;
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs	(revision 8766)
@@ -0,0 +1,272 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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 System.ComponentModel;
+using System.Linq;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  public class FeatureCorrelationCalculator : Object {
+
+    private BackgroundWorker bw;
+    private BackgroundWorkerInfo bwInfo;
+
+    private IDataAnalysisProblemData problemData;
+    public IDataAnalysisProblemData ProblemData {
+      set {
+        if (bw != null) {
+          bw.CancelAsync();
+        }
+        problemData = value;
+      }
+    }
+
+    public FeatureCorrelationCalculator()
+      : base() { }
+
+    public FeatureCorrelationCalculator(IDataAnalysisProblemData problemData)
+      : base() {
+      this.problemData = problemData;
+    }
+
+    public void CalculateElements(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition) {
+      CalculateElements(problemData.Dataset, calc, partition);
+    }
+
+    // returns if any calculation takes place
+    public bool CalculateTimeframeElements(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable, int frames, double[,] correlation = null) {
+      if (correlation == null || correlation.GetLength(1) <= frames) {
+        CalculateElements(problemData.Dataset, calc, partition, variable, frames, correlation);
+        return true;
+      } else {
+        return false;
+      }
+    }
+
+    private double[,] GetElementsOfCorrelation(double[,] corr, int frames) {
+      double[,] elements = new double[corr.GetLength(0), frames + 1];
+      for (int i = 0; i < corr.GetLength(0); i++) {
+        for (int j = 0; j <= frames; j++) {
+          elements[i, j] = corr[i, j];
+        }
+      }
+      return elements;
+    }
+
+    private void CalculateElements(Dataset dataset, FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable = null, int frames = 0, double[,] alreadyCalculated = null) {
+      bwInfo = new BackgroundWorkerInfo { Dataset = dataset, Calculator = calc, Partition = partition, Variable = variable, Frames = frames, AlreadyCalculated = alreadyCalculated };
+      if (bw == null) {
+        bw = new BackgroundWorker();
+        bw.WorkerReportsProgress = true;
+        bw.WorkerSupportsCancellation = true;
+        bw.DoWork += new DoWorkEventHandler(BwDoWork);
+        bw.ProgressChanged += new ProgressChangedEventHandler(BwProgressChanged);
+        bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(BwRunWorkerCompleted);
+      }
+      if (bw.IsBusy) {
+        bw.CancelAsync();
+      } else {
+        bw.RunWorkerAsync(bwInfo);
+      }
+    }
+
+    #region backgroundworker
+    private void BwDoWork(object sender, DoWorkEventArgs e) {
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      if (bwInfo.Variable == null) {
+        BwCalculateCorrelation(sender, e);
+      } else {
+        BwCalculateTimeframeCorrelation(sender, e);
+      }
+    }
+
+    private void BwCalculateCorrelation(object sender, DoWorkEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      Dataset dataset = bwInfo.Dataset;
+      FeatureCorrelationEnums.Partitions partition = bwInfo.Partition;
+      FeatureCorrelationEnums.CorrelationCalculators calc = bwInfo.Calculator;
+
+      IList<string> doubleVariableNames = dataset.DoubleVariables.ToList();
+      OnlineCalculatorError error = OnlineCalculatorError.None;
+      int length = doubleVariableNames.Count;
+      double[,] elements = new double[length, length];
+      double calculations = (Math.Pow(length, 2) + length) / 2;
+
+      worker.ReportProgress(0);
+
+      for (int i = 0; i < length; i++) {
+        for (int j = 0; j < i + 1; j++) {
+          if (worker.CancellationPending) {
+            e.Cancel = true;
+            return;
+          }
+          IEnumerable<double> var1 = GetRelevantValues(problemData, partition, doubleVariableNames[i]);
+          IEnumerable<double> var2 = GetRelevantValues(problemData, partition, doubleVariableNames[j]);
+
+          elements[i, j] = CalculateElementWithCalculator(calc, var1, var2, out error);
+
+          if (!error.Equals(OnlineCalculatorError.None)) {
+            elements[i, j] = double.NaN;
+          }
+          elements[j, i] = elements[i, j];
+          worker.ReportProgress((int)Math.Round((((Math.Pow(i, 2) + i) / 2 + j + 1.0) / calculations) * 100));
+        }
+      }
+      e.Result = elements;
+    }
+
+    private void BwCalculateTimeframeCorrelation(object sender, DoWorkEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      Dataset dataset = bwInfo.Dataset;
+      FeatureCorrelationEnums.Partitions partition = bwInfo.Partition;
+      FeatureCorrelationEnums.CorrelationCalculators calc = bwInfo.Calculator;
+      string variable = bwInfo.Variable;
+      int frames = bwInfo.Frames;
+      double[,] alreadyCalculated = bwInfo.AlreadyCalculated;
+
+      IList<string> doubleVariableNames = dataset.DoubleVariables.ToList();
+      OnlineCalculatorError error = OnlineCalculatorError.None;
+      int length = doubleVariableNames.Count;
+      double[,] elements = new double[length, frames + 1];
+      double calculations = (frames + 1) * length;
+
+      worker.ReportProgress(0);
+
+      int start = 0;
+      if (alreadyCalculated != null) {
+        for (int i = 0; i < alreadyCalculated.GetLength(0); i++) {
+          Array.Copy(alreadyCalculated, i * alreadyCalculated.GetLength(1), elements, i * elements.GetLength(1), alreadyCalculated.GetLength(1));
+        }
+        start = alreadyCalculated.GetLength(1);
+      }
+
+      for (int i = 0; i < length; i++) {
+        for (int j = start; j <= frames; j++) {
+          if (worker.CancellationPending) {
+            e.Cancel = true;
+            return;
+          }
+
+          IEnumerable<double> var1 = GetRelevantValues(problemData, partition, variable);
+          IEnumerable<double> var2 = GetRelevantValues(problemData, partition, doubleVariableNames[i]);
+
+          var valuesInFrame = var1.Take(j);
+          var help = var1.Skip(j).ToList();
+          help.AddRange(valuesInFrame);
+          var1 = help;
+
+          elements[i, j] = CalculateElementWithCalculator(calc, var1, var2, out error);
+
+          if (!error.Equals(OnlineCalculatorError.None)) {
+            elements[i, j] = double.NaN;
+          }
+          worker.ReportProgress((int)((100.0 / calculations) * (i * (frames + 1) + j + 1)));
+        }
+      }
+      e.Result = elements;
+    }
+
+    private IEnumerable<double> GetRelevantValues(IDataAnalysisProblemData problemData, FeatureCorrelationEnums.Partitions partition, string variable) {
+      IEnumerable<double> var = problemData.Dataset.GetDoubleValues(variable);
+      if (partition.Equals(FeatureCorrelationEnums.Partitions.TrainingSamples)) {
+        var = var.Skip(problemData.TrainingPartition.Start).Take(problemData.TrainingPartition.End - problemData.TrainingPartition.Start);
+      } else if (partition.Equals(FeatureCorrelationEnums.Partitions.TestSamples)) {
+        var = var.Skip(problemData.TestPartition.Start).Take(problemData.TestPartition.End - problemData.TestPartition.Start);
+      }
+      return var;
+    }
+
+    private double CalculateElementWithCalculator(FeatureCorrelationEnums.CorrelationCalculators calc, IEnumerable<double> var1, IEnumerable<double> var2, out OnlineCalculatorError error) {
+      if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.HoeffdingsDependence)) {
+        return HoeffdingsDependenceCalculator.Calculate(var1, var2, out error);
+      } else if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.SpearmansRank)) {
+        return SpearmansRankCorrelationCoefficientCalculator.Calculate(var1, var2, out error);
+      } else if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.PearsonsRSquared)) {
+        return OnlinePearsonsRSquaredCalculator.Calculate(var1, var2, out error);
+      } else {
+        return Math.Sqrt(OnlinePearsonsRSquaredCalculator.Calculate(var1, var2, out error));
+      }
+    }
+
+    private void BwRunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+      if (!e.Cancelled && !worker.CancellationPending) {
+        if (!(e.Error == null)) {
+          ErrorHandling.ShowErrorDialog(e.Error);
+        } else {
+          OnCorrelationCalculationFinished((double[,])e.Result, bwInfo.Calculator, bwInfo.Partition, bwInfo.Variable);
+        }
+      } else {
+        bw.RunWorkerAsync(bwInfo);
+      }
+    }
+    #endregion
+
+    #region events
+    public class CorrelationCalculationFinishedArgs : EventArgs {
+      public double[,] Correlation { get; private set; }
+      public FeatureCorrelationEnums.CorrelationCalculators Calculcator { get; private set; }
+      public FeatureCorrelationEnums.Partitions Partition { get; private set; }
+      public string Variable { get; private set; }
+
+      public CorrelationCalculationFinishedArgs(double[,] correlation, FeatureCorrelationEnums.CorrelationCalculators calculator, FeatureCorrelationEnums.Partitions partition, string variable = null) {
+        this.Correlation = correlation;
+        this.Calculcator = calculator;
+        this.Partition = partition;
+        this.Variable = variable;
+      }
+    }
+
+    public delegate void CorrelationCalculationFinishedHandler(object sender, CorrelationCalculationFinishedArgs e);
+    public event CorrelationCalculationFinishedHandler CorrelationCalculationFinished;
+    protected virtual void OnCorrelationCalculationFinished(double[,] correlation, FeatureCorrelationEnums.CorrelationCalculators calculator, FeatureCorrelationEnums.Partitions partition, string variable = null) {
+      var handler = CorrelationCalculationFinished;
+      if (handler != null)
+        handler(this, new CorrelationCalculationFinishedArgs(correlation, calculator, partition, variable));
+    }
+
+    public delegate void ProgressCalculationHandler(object sender, ProgressChangedEventArgs e);
+    public event ProgressCalculationHandler ProgressCalculation;
+    protected void BwProgressChanged(object sender, ProgressChangedEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+      if (!worker.CancellationPending && ProgressCalculation != null) {
+        ProgressCalculation(sender, e);
+      }
+    }
+    #endregion
+
+    private class BackgroundWorkerInfo {
+      public Dataset Dataset { get; set; }
+      public FeatureCorrelationEnums.CorrelationCalculators Calculator { get; set; }
+      public FeatureCorrelationEnums.Partitions Partition { get; set; }
+      public string Variable { get; set; }
+      public int Frames { get; set; }
+      public double[,] AlreadyCalculated { get; set; }
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs	(revision 8766)
@@ -0,0 +1,103 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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 System.ComponentModel;
+using System.Linq;
+using System.Reflection;
+using HeuristicLab.Data;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  // see http://blog.spontaneouspublicity.com/associating-strings-with-enums-in-c
+  [NonDiscoverableType]
+  public class FeatureCorrelationEnums {
+    public enum Partitions {
+      [Description("All Samples")]
+      AllSamples,
+      [Description("Training Samples")]
+      TrainingSamples,
+      [Description("Test Samples")]
+      TestSamples
+    }
+
+    public enum CorrelationCalculators {
+      [Description("Pearsons R")]
+      PearsonsR,
+      [Description("Pearsons R Squared")]
+      PearsonsRSquared,
+      [Description("Hoeffdings Dependence")]
+      HoeffdingsDependence,
+      [Description("Spearmans Rank")]
+      SpearmansRank
+    }
+
+    public static readonly Dictionary<CorrelationCalculators, DoubleRange> calculatorInterval = new Dictionary<CorrelationCalculators, DoubleRange>(){ 
+      { CorrelationCalculators.PearsonsR, new DoubleRange(1.0, -1.0)},
+      { CorrelationCalculators.SpearmansRank, new DoubleRange(1.0, -1.0)},
+      { CorrelationCalculators.HoeffdingsDependence, new DoubleRange(1.0, -0.5)},
+      { CorrelationCalculators.PearsonsRSquared, new DoubleRange(1.0, 0.0)}
+    };
+
+    public static string GetEnumDescription(object value) {
+      FieldInfo fi = value.GetType().GetField(value.ToString());
+
+      DescriptionAttribute[] attributes =
+          (DescriptionAttribute[])fi.GetCustomAttributes(typeof(DescriptionAttribute), false);
+
+      if (attributes != null && attributes.Length > 0)
+        return attributes[0].Description;
+      else
+        return value.ToString();
+    }
+
+    public static IEnumerable<T> EnumToList<T>() {
+      Type enumType = typeof(T);
+
+      // Can't use generic type constraints on value types,
+      // so have to do check like this
+      if (enumType.BaseType != typeof(Enum)) {
+        throw new ArgumentException("T must be of type System.Enum");
+      }
+
+      Array enumValArray = Enum.GetValues(enumType);
+      return Enum.GetValues(enumType).Cast<int>().Select(x => (T)Enum.Parse(enumType, x.ToString())).ToList();
+    }
+
+    public static T GetEnumOfDescription<T>(string desc) {
+      Type enumType = typeof(T);
+
+      if (enumType.BaseType != typeof(Enum))
+        throw new ArgumentException("T must be of type System.Enum");
+
+      Array enumValArray = Enum.GetValues(enumType);
+      foreach (int val in enumValArray) {
+
+        T e = (T)Enum.Parse(enumType, val.ToString());
+        if (GetEnumDescription(e).Equals(desc)) {
+          return (T)e;
+        }
+      }
+      throw new ArgumentException("Description is not in the given type T");
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs	(revision 8766)
@@ -0,0 +1,61 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  internal class FeatureCorrelationTimeframeCache : Object {
+    private Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>> timeFrameCorrelationsCache;
+
+    public FeatureCorrelationTimeframeCache()
+      : base() {
+      InitializeCaches();
+    }
+
+    private void InitializeCaches() {
+      timeFrameCorrelationsCache = new Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>>();
+      foreach (var calc in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>()) {
+        timeFrameCorrelationsCache.Add(calc, new Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>());
+        foreach (var part in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.Partitions>()) {
+          timeFrameCorrelationsCache[calc].Add(part, new Dictionary<string, double[,]>());
+        }
+      }
+    }
+
+    public void Reset() {
+      InitializeCaches();
+    }
+
+    public double[,] GetTimeframeCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable) {
+      double[,] corr;
+      timeFrameCorrelationsCache[calc][partition].TryGetValue(variable, out corr);
+      return corr;
+    }
+
+    public void SetTimeframeCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable, double[,] correlation) {
+      timeFrameCorrelationsCache[calc][partition][variable] = correlation;
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs	(revision 8766)
@@ -0,0 +1,60 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class FeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.SuspendLayout();
+      // 
+      // FeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "FeatureCorrelationView";
+      this.Size = new System.Drawing.Size(569, 336);
+      this.ResumeLayout(false);
+    }
+    #endregion
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs	(revision 8766)
@@ -0,0 +1,85 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Data;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public partial class FeatureCorrelationView : AbstractFeatureCorrelationView {
+
+    private FeatureCorrelationCache correlationCache;
+
+    public FeatureCorrelationView()
+      : base() {
+      InitializeComponent();
+      correlationCache = new FeatureCorrelationCache();
+    }
+
+    protected override void OnContentChanged() {
+      correlationCache.Reset();
+      base.OnContentChanged();
+    }
+
+    protected override void CalculateCorrelation() {
+      if (CorrelationCalcComboBox.SelectedItem != null && PartitionComboBox.SelectedItem != null) {
+        FeatureCorrelationEnums.CorrelationCalculators calc = (FeatureCorrelationEnums.CorrelationCalculators)CorrelationCalcComboBox.SelectedValue;
+        FeatureCorrelationEnums.Partitions partition = (FeatureCorrelationEnums.Partitions)PartitionComboBox.SelectedValue;
+        DataGridView.Columns.Clear();
+        DataGridView.Enabled = false;
+        double[,] corr = correlationCache.GetCorrelation(calc, partition);
+        if (corr == null) {
+          fcc.CalculateElements(calc, partition);
+        } else {
+          SetNewCorrelation(corr, calc);
+          UpdateDataGrid();
+        }
+      }
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc) {
+      DoubleRange range = FeatureCorrelationEnums.calculatorInterval[calc];
+      HeatMap hm = new HeatMap(elements, "", range.End, range.Start);
+      hm.RowNames = Content.Dataset.DoubleVariables;
+      hm.ColumnNames = Content.Dataset.DoubleVariables;
+      currentCorrelation = hm;
+    }
+
+    protected override void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
+      if (InvokeRequired) {
+        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
+      } else {
+        correlationCache.SetCorrelation(e.Calculcator, e.Partition, e.Correlation);
+        SetNewCorrelation(e.Correlation, e.Calculcator);
+        UpdateDataGrid();
+      }
+    }
+
+    protected override void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e) {
+      DataGridView.Columns[e.Index].Visible = e.NewValue == CheckState.Checked;
+      DataGridView.Rows[GetRowIndexOfVirtualindex(e.Index)].Visible = e.NewValue == CheckState.Checked;
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs	(revision 8766)
@@ -0,0 +1,150 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class TimeframeFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.VariableSelectionLabel = new System.Windows.Forms.Label();
+      this.VariableSelectionComboBox = new System.Windows.Forms.ComboBox();
+      this.TimeFrameLabel = new System.Windows.Forms.Label();
+      this.TimeframeComboBox = new System.Windows.Forms.ComboBox();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).BeginInit();
+      this.SplitContainer.Panel1.SuspendLayout();
+      this.SplitContainer.Panel2.SuspendLayout();
+      this.SplitContainer.SuspendLayout();
+      this.CalculatingPanel.SuspendLayout();
+      this.SuspendLayout();
+      // 
+      // PartitionComboBox
+      // 
+      this.PartitionComboBox.Location = new System.Drawing.Point(344, 3);
+      this.PartitionComboBox.Size = new System.Drawing.Size(131, 21);
+      // 
+      // maximumLabel
+      // 
+      this.maximumLabel.Location = new System.Drawing.Point(487, 3);
+      // 
+      // PictureBox
+      // 
+      this.PictureBox.Location = new System.Drawing.Point(506, 31);
+      this.PictureBox.Size = new System.Drawing.Size(35, 280);
+      // 
+      // SplitContainer
+      // 
+      // 
+      // SplitContainer.Panel1
+      // 
+      this.SplitContainer.Panel1.Controls.Add(this.VariableSelectionComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.TimeframeComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.TimeFrameLabel);
+      this.SplitContainer.Panel1.Controls.Add(this.VariableSelectionLabel);
+      this.SplitContainer.SplitterDistance = 52;
+      // 
+      // CalculatingPanel
+      // 
+      this.CalculatingPanel.Location = new System.Drawing.Point(138, 82);
+      // 
+      // VariableSelectionLabel
+      // 
+      this.VariableSelectionLabel.AutoSize = true;
+      this.VariableSelectionLabel.Location = new System.Drawing.Point(0, 33);
+      this.VariableSelectionLabel.Name = "VariableSelectionLabel";
+      this.VariableSelectionLabel.Size = new System.Drawing.Size(81, 13);
+      this.VariableSelectionLabel.TabIndex = 16;
+      this.VariableSelectionLabel.Text = "Select Variable:";
+      // 
+      // VariableSelectionComboBox
+      // 
+      this.VariableSelectionComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.VariableSelectionComboBox.FormattingEnabled = true;
+      this.VariableSelectionComboBox.Location = new System.Drawing.Point(110, 30);
+      this.VariableSelectionComboBox.Name = "VariableSelectionComboBox";
+      this.VariableSelectionComboBox.Size = new System.Drawing.Size(163, 21);
+      this.VariableSelectionComboBox.TabIndex = 17;
+      this.VariableSelectionComboBox.SelectionChangeCommitted += new System.EventHandler(this.VariableSelectionComboBox_SelectedChangeCommitted);
+      // 
+      // TimeFrameLabel
+      // 
+      this.TimeFrameLabel.AutoSize = true;
+      this.TimeFrameLabel.Location = new System.Drawing.Point(279, 33);
+      this.TimeFrameLabel.Name = "TimeFrameLabel";
+      this.TimeFrameLabel.Size = new System.Drawing.Size(59, 13);
+      this.TimeFrameLabel.TabIndex = 18;
+      this.TimeFrameLabel.Text = "Timeframe:";
+      // 
+      // TimeframeComboBox
+      // 
+      this.TimeframeComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.TimeframeComboBox.FormattingEnabled = true;
+      this.TimeframeComboBox.Location = new System.Drawing.Point(344, 30);
+      this.TimeframeComboBox.Name = "TimeframeComboBox";
+      this.TimeframeComboBox.Size = new System.Drawing.Size(131, 21);
+      this.TimeframeComboBox.TabIndex = 19;
+      this.TimeframeComboBox.SelectionChangeCommitted += new System.EventHandler(this.TimeframeComboBox_SelectedChangeCommitted);
+      // 
+      // TimeframeFeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "TimeframeFeatureCorrelationView";
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).EndInit();
+      this.SplitContainer.Panel1.ResumeLayout(false);
+      this.SplitContainer.Panel1.PerformLayout();
+      this.SplitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).EndInit();
+      this.SplitContainer.ResumeLayout(false);
+      this.CalculatingPanel.ResumeLayout(false);
+      this.CalculatingPanel.PerformLayout();
+      this.ResumeLayout(false);
+
+    }
+    #endregion
+
+    protected System.Windows.Forms.Label VariableSelectionLabel;
+    protected System.Windows.Forms.ComboBox VariableSelectionComboBox;
+    protected System.Windows.Forms.Label TimeFrameLabel;
+    protected System.Windows.Forms.ComboBox TimeframeComboBox;
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs	(revision 8766)
@@ -0,0 +1,113 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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.Linq;
+using System.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Data;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Timeframe Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public partial class TimeframeFeatureCorrelationView : AbstractFeatureCorrelationView {
+
+    private FeatureCorrelationTimeframeCache correlationTimeframCache;
+
+    public TimeframeFeatureCorrelationView() {
+      InitializeComponent();
+      TimeframeComboBox.DataSource = Enumerable.Range(0, 16).ToList<int>();
+      correlationTimeframCache = new FeatureCorrelationTimeframeCache();
+    }
+
+    protected override void OnContentChanged() {
+      correlationTimeframCache.Reset();
+      if (Content != null) {
+        VariableSelectionComboBox.DataSource = Content.Dataset.DoubleVariables.ToList();
+      }
+      base.OnContentChanged();
+    }
+
+    protected void VariableSelectionComboBox_SelectedChangeCommitted(object sender, EventArgs e) {
+      CalculateCorrelation();
+    }
+    protected void TimeframeComboBox_SelectedChangeCommitted(object sender, EventArgs e) {
+      CalculateCorrelation();
+    }
+
+    protected override void CalculateCorrelation() {
+      string variable = (string)VariableSelectionComboBox.SelectedItem;
+      if (CorrelationCalcComboBox.SelectedItem != null && PartitionComboBox.SelectedItem != null && variable != null) {
+        FeatureCorrelationEnums.CorrelationCalculators calc = (FeatureCorrelationEnums.CorrelationCalculators)CorrelationCalcComboBox.SelectedValue;
+        FeatureCorrelationEnums.Partitions partition = (FeatureCorrelationEnums.Partitions)PartitionComboBox.SelectedValue;
+        DataGridView.Columns.Clear();
+        DataGridView.Enabled = false;
+        int frames = (int)TimeframeComboBox.SelectedItem;
+        double[,] corr = correlationTimeframCache.GetTimeframeCorrelation(calc, partition, variable);
+        if (corr == null) {
+          fcc.CalculateTimeframeElements(calc, partition, variable, frames);
+        } else if (corr.GetLength(1) <= frames) {
+          fcc.CalculateTimeframeElements(calc, partition, variable, frames, corr);
+        } else {
+          SetNewCorrelation(corr, calc, frames);
+          UpdateDataGrid();
+        }
+      }
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc, int frames) {
+      double[,] neededValues = new double[elements.GetLength(0), frames + 1];
+      for (int i = 0; i < elements.GetLength(0); i++) {
+        Array.Copy(elements, i * elements.GetLength(1), neededValues, i * neededValues.GetLength(1), frames + 1);
+      }
+      SetNewCorrelation(neededValues, calc);
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc) {
+      DoubleRange range = FeatureCorrelationEnums.calculatorInterval[calc];
+      HeatMap hm = new HeatMap(elements, "", range.End, range.Start);
+      hm.RowNames = Content.Dataset.DoubleVariables;
+      hm.ColumnNames = Enumerable.Range(0, elements.GetLength(1)).Select(x => x.ToString());
+      currentCorrelation = hm;
+    }
+
+    protected override void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
+      if (InvokeRequired) {
+        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
+      } else {
+        correlationTimeframCache.SetTimeframeCorrelation(e.Calculcator, e.Partition, e.Variable, e.Correlation);
+        SetNewCorrelation(e.Correlation, e.Calculcator);
+        UpdateDataGrid();
+      }
+    }
+
+    protected override void UpdateColumnHeaders() {
+      for (int i = 0; i < DataGridView.ColumnCount; i++) {
+        DataGridView.Columns[i].HeaderText = i.ToString();
+      }
+    }
+
+    protected override void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e) {
+      DataGridView.Rows[GetRowIndexOfVirtualindex(e.Index)].Visible = e.NewValue == CheckState.Checked;
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs	(revision 8766)
@@ -0,0 +1,112 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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
+
+namespace HeuristicLab.DataAnalysis.Views {
+  partial class VariableVisibilityDialog {
+    /// <summary>
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary>
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Windows Form Designer generated code
+
+    /// <summary>
+    /// Required method for Designer support - do not modify
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.checkedListBox = new System.Windows.Forms.CheckedListBox();
+      this.btnShowAll = new System.Windows.Forms.Button();
+      this.btnHideAll = new System.Windows.Forms.Button();
+      this.SuspendLayout();
+      // 
+      // checkedListBox
+      // 
+      this.checkedListBox.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Left)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.checkedListBox.CheckOnClick = true;
+      this.checkedListBox.FormattingEnabled = true;
+      this.checkedListBox.Location = new System.Drawing.Point(12, 12);
+      this.checkedListBox.Name = "checkedListBox";
+      this.checkedListBox.Size = new System.Drawing.Size(309, 289);
+      this.checkedListBox.TabIndex = 0;
+      this.checkedListBox.ItemCheck += new System.Windows.Forms.ItemCheckEventHandler(this.checkedListBox_ItemCheck);
+      // 
+      // btnShowAll
+      // 
+      this.btnShowAll.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Left)));
+      this.btnShowAll.Location = new System.Drawing.Point(12, 315);
+      this.btnShowAll.Name = "btnShowAll";
+      this.btnShowAll.Size = new System.Drawing.Size(75, 23);
+      this.btnShowAll.TabIndex = 1;
+      this.btnShowAll.Text = "Show all";
+      this.btnShowAll.UseVisualStyleBackColor = true;
+      this.btnShowAll.Click += new System.EventHandler(this.btnShowAll_Click);
+      // 
+      // btnHideAll
+      // 
+      this.btnHideAll.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Left)));
+      this.btnHideAll.Location = new System.Drawing.Point(101, 315);
+      this.btnHideAll.Name = "btnHideAll";
+      this.btnHideAll.Size = new System.Drawing.Size(75, 23);
+      this.btnHideAll.TabIndex = 2;
+      this.btnHideAll.Text = "Hide all";
+      this.btnHideAll.UseVisualStyleBackColor = true;
+      this.btnHideAll.Click += new System.EventHandler(this.btnHideAll_Click);
+      // 
+      // ColumnsVisibilityDialog
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit;
+      this.ClientSize = new System.Drawing.Size(332, 350);
+      this.Controls.Add(this.btnHideAll);
+      this.Controls.Add(this.btnShowAll);
+      this.Controls.Add(this.checkedListBox);
+      this.MaximizeBox = false;
+      this.MinimizeBox = false;
+      this.Name = "ColumnsVisibilityDialog";
+      this.ShowIcon = false;
+      this.ShowInTaskbar = false;
+      this.StartPosition = System.Windows.Forms.FormStartPosition.CenterParent;
+      this.Text = "Show/Hide Columns";
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+
+    private System.Windows.Forms.CheckedListBox checkedListBox;
+    private System.Windows.Forms.Button btnShowAll;
+    private System.Windows.Forms.Button btnHideAll;
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs	(revision 8766)
@@ -0,0 +1,76 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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 System.Linq;
+using System.Windows.Forms;
+
+namespace HeuristicLab.DataAnalysis.Views {
+  public partial class VariableVisibilityDialog : Form {
+    public VariableVisibilityDialog() {
+      InitializeComponent();
+    }
+
+    public VariableVisibilityDialog(IEnumerable<string> columns, IEnumerable<bool> visibility)
+      : this() {
+      if (columns.Count() != visibility.Count()) {
+        throw new ArgumentException("There have to be as many variables as values for visibility!");
+      }
+      this.columns = columns.ToList();
+      this.visibility = visibility.ToList();
+      UpdateCheckBoxes();
+    }
+
+    private List<string> columns;
+    public IList<string> Columns {
+      get { return this.columns; }
+    }
+    private List<bool> visibility;
+    public IList<bool> Visibility {
+      get { return this.visibility; }
+    }
+
+    private void UpdateCheckBoxes() {
+      for (int i = 0; i < columns.Count; i++) {
+        checkedListBox.Items.Add(columns[i], visibility[i]);
+      }
+    }
+
+    public event ItemCheckEventHandler VariableVisibilityChanged;
+
+    private void checkedListBox_ItemCheck(object sender, ItemCheckEventArgs e) {
+      this.visibility[e.Index] = e.NewValue == CheckState.Checked;
+      if (VariableVisibilityChanged != null) {
+        VariableVisibilityChanged(this, e);
+      }
+    }
+
+    private void btnShowAll_Click(object sender, System.EventArgs e) {
+      for (int i = 0; i < checkedListBox.Items.Count; i++)
+        checkedListBox.SetItemChecked(i, true);
+    }
+    private void btnHideAll_Click(object sender, System.EventArgs e) {
+      for (int i = 0; i < checkedListBox.Items.Count; i++)
+        checkedListBox.SetItemChecked(i, false);
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Mammography.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Mammography.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Mammography.cs	(revision 8766)
@@ -0,0 +1,49 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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;
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class Mammography : IDataDescriptor {
+    public string Name { get { return "mammography"; } }
+    public string Description {
+      get {
+        return "Data Set Information: Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass."
+        + "Website: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29" + Environment.NewLine
+        + "Attribute Information:" + Environment.NewLine
+        + "1) ID number" + Environment.NewLine
+        + "2) Diagnosis (M = malignant, B = benign)" + Environment.NewLine
+        + "3-32)" + Environment.NewLine + Environment.NewLine
+        + "Ten real-valued features are computed for each cell nucleus:" + Environment.NewLine + Environment.NewLine
+        + "a) radius (mean of distances from center to points on the perimeter)" + Environment.NewLine
+        + "b) texture (standard deviation of gray-scale values)" + Environment.NewLine
+        + "c) perimeter" + Environment.NewLine
+        + "d) area" + Environment.NewLine
+        + "e) smoothness (local variation in radius lengths)" + Environment.NewLine
+        + "f) compactness (perimeter^2 / area - 1.0)" + Environment.NewLine
+        + "g) concavity (severity of concave portions of the contour)" + Environment.NewLine
+        + "h) concave points (number of concave portions of the contour)" + Environment.NewLine
+        + "i) symmetry" + Environment.NewLine
+        + "j) fractal dimension (\"coastline approximation\" - 1)";
+      }
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/UCIInstanceProvider.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/UCIInstanceProvider.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/UCIInstanceProvider.cs	(revision 8766)
@@ -0,0 +1,71 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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 System.IO;
+using System.Linq;
+using ICSharpCode.SharpZipLib.Zip;
+
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class UCIInstanceProvider : ResourceClassificationInstanceProvider {
+    public override string Name {
+      get { return "UCI Problems"; }
+    }
+    public override string Description {
+      get {
+        return "";
+      }
+    }
+    public override Uri WebLink {
+      get { return new Uri("http://archive.ics.uci.edu/ml/datasets.html"); }
+    }
+    public override string ReferencePublication {
+      get { return ""; }
+    }
+
+    protected override string FileName { get { return "UCI"; } }
+
+    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
+      List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
+      descriptorList.Add(new Mammography());
+      descriptorList.Add(new Wine());
+      var solutionsArchiveName = GetResourceName(FileName + @"\.zip");
+      if (!String.IsNullOrEmpty(solutionsArchiveName)) {
+        using (var solutionsZipFile = new ZipInputStream(GetType().Assembly.GetManifestResourceStream(solutionsArchiveName))) {
+          IList<string> entries = new List<string>();
+          ZipEntry curEntry;
+          while ((curEntry = solutionsZipFile.GetNextEntry()) != null) {
+            entries.Add(curEntry.Name);
+          }
+          foreach (var entry in entries.OrderBy(x => x)) {
+            string prettyName = Path.GetFileNameWithoutExtension(entry);
+            IDataDescriptor desc = descriptorList.Where(x => x.Name.Equals(prettyName)).FirstOrDefault();
+            if (desc != null) {
+              yield return new ResourceClassificationDataDescriptor(prettyName, desc.Description, entry);
+            } else
+              yield return new ResourceClassificationDataDescriptor(prettyName, Description, entry);
+          }
+        }
+      }
+    }
+  }
+}
Index: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Wine.cs
===================================================================
--- branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Wine.cs	(revision 8766)
+++ branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/UCI/Wine.cs	(revision 8766)
@@ -0,0 +1,54 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 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;
+namespace HeuristicLab.Problems.Instances.DataAnalysis {
+  public class Wine : IDataDescriptor {
+    public string Name { get { return "Wine"; } }
+    public string Description {
+      get {
+        return "These data are the results of a chemical analysis of wines grown in the same region " +
+        "in Italy but derived from three different cultivars. The analysis determined the quantities " +
+        "of 13 constituents found in each of the three types of wines." + Environment.NewLine + Environment.NewLine +
+        "I think that the initial data set had around 30 variables, but for some reason I only have the " +
+        "13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), " +
+        "I would not know which 13 variables are included in the set." + Environment.NewLine + Environment.NewLine +
+        "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it )" + Environment.NewLine +
+        "1) Alcohol" + Environment.NewLine +
+        "2) Malic acid" + Environment.NewLine +
+        "3) Ash" + Environment.NewLine +
+        "4) Alcalinity of ash" + Environment.NewLine +
+        "5) Magnesium" + Environment.NewLine +
+        "6) Total phenols" + Environment.NewLine +
+        "7) Flavanoids" + Environment.NewLine +
+        "8) Nonflavanoid phenols" + Environment.NewLine +
+        "9) Proanthocyanins" + Environment.NewLine +
+        "10)Color intensity" + Environment.NewLine +
+        "11)Hue" + Environment.NewLine +
+        "12)OD280/OD315 of diluted wines" + Environment.NewLine +
+        "13)Proline" + Environment.NewLine + Environment.NewLine +
+        "In a classification context, this is a well posed problem with \"well behaved\" class structures. A " +
+        "good data set for first testing of a new classifier, but not very challenging. ";
+      }
+    }
+  }
+}
