Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanModel.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanModel.cs	(revision 13120)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanModel.cs	(revision 13120)
@@ -0,0 +1,98 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2015 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.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  [StorableClass]
+  [Item(Name = "MeanModel", Description = "A mean function for Gaussian processes that uses a regression solution created with a different algorithm to calculate the mean.")]
+  // essentially an adaptor which maps form IMeanFunction to IRegressionSolution
+  public sealed class MeanModel : ParameterizedNamedItem, IMeanFunction {
+    public IValueParameter<IRegressionSolution> RegressionSolutionParameter {
+      get { return (IValueParameter<IRegressionSolution>)Parameters["RegressionSolution"]; }
+    }
+    public IRegressionSolution RegressionSolution {
+      get { return RegressionSolutionParameter.Value; }
+      set { RegressionSolutionParameter.Value = value; }
+    }
+
+    [StorableConstructor]
+    private MeanModel(bool deserializing) : base(deserializing) { }
+    private MeanModel(MeanModel original, Cloner cloner)
+      : base(original, cloner) {
+    }
+
+    public MeanModel()
+      : base("MeanModel", "A mean function for Gaussian processes that uses a regression solution created with a different algorithm to calculate the mean.") {
+      Parameters.Add(new ValueParameter<IRegressionSolution>("RegressionSolution", "The solution containing the model that should be used for the mean prediction."));
+    }
+
+    public MeanModel(IRegressionSolution solution)
+      : this() {
+      // here we cannot check if the model is actually compatible (uses only input variables that are available)
+      // we only assume that the list of allowed inputs in the regression solution is the same as the list of allowed
+      // inputs in the Gaussian process.
+      // later we might get an error or bad behaviour when the mean function is evaluated
+      RegressionSolution = solution;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new MeanModel(this, cloner);
+    }
+
+    public int GetNumberOfParameters(int numberOfVariables) {
+      return 0; // no support for hyperparameters for regression models yet
+    }
+
+    public void SetParameter(double[] p) {
+      if (p.Length > 0) throw new ArgumentException("No parameters allowed for model-based mean function.", "p");
+    }
+
+    public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
+      if (p.Length > 0) throw new ArgumentException("No parameters allowed for model-based mean function.", "p");
+      var solution = RegressionSolution;
+      var variableNames = solution.ProblemData.AllowedInputVariables.ToArray();
+      if (variableNames.Length != columnIndices.Count())
+        throw new ArgumentException("The number of input variables does not match in MeanModel");
+      var variableValues = variableNames.Select(_ => new List<double>() { 0.0 }).ToArray(); // or of zeros
+      // uses modifyable dataset to pass values to the model
+      var ds = new ModifiableDataset(variableNames, variableValues);
+      var mf = new ParameterizedMeanFunction();
+      var model = solution.Model; // effort for parameter access only once
+      mf.Mean = (x, i) => {
+        ds.ReplaceRow(0, Util.GetRow(x, i, columnIndices).OfType<object>());
+        return model.GetEstimatedValues(ds, 0.ToEnumerable()).Single(); // evaluate the model on the specified row only
+      };
+      mf.Gradient = (x, i, k) => {
+        if (k > 0)
+          throw new ArgumentException();
+        return 0.0;
+      };
+      return mf;
+    }
+  }
+}
