Index: trunk/sources/HeuristicLab.Problems.GeneticProgramming/3.3/BasicSymbolicRegression/Problem.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.GeneticProgramming/3.3/BasicSymbolicRegression/Problem.cs	(revision 12937)
+++ trunk/sources/HeuristicLab.Problems.GeneticProgramming/3.3/BasicSymbolicRegression/Problem.cs	(revision 12937)
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+#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.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.Instances;
+
+
+namespace HeuristicLab.Problems.GeneticProgramming.BasicSymbolicRegression {
+  [Item("Koza-style Symbolic Regression", "An implementation of symbolic regression without bells-and-whistles. Use \"Symbolic Regression Problem (single-objective)\" if you want to use all features.")]
+  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 900)]
+  [StorableClass]
+  public sealed class Problem : SymbolicExpressionTreeProblem, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
+
+    #region parameter names
+
+    private const string ProblemDataParameterName = "ProblemData";
+
+    #endregion
+
+    public event EventHandler ProblemDataChanged;
+
+    #region Parameter Properties
+    IParameter IDataAnalysisProblem.ProblemDataParameter { get { return ProblemDataParameter; } }
+
+    public IValueParameter<IRegressionProblemData> ProblemDataParameter {
+      get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
+    }
+
+    #endregion
+
+    #region Properties
+
+    public IRegressionProblemData ProblemData {
+      get { return ProblemDataParameter.Value; }
+      set { ProblemDataParameter.Value = value; }
+    }
+    IDataAnalysisProblemData IDataAnalysisProblem.ProblemData { get { return ProblemData; } }
+
+
+    #endregion
+
+    public override bool Maximization {
+      get { return true; }
+    }
+
+    public Problem()
+      : base() {
+      Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, "The data for the regression problem", new RegressionProblemData()));
+
+      var g = new SimpleSymbolicExpressionGrammar(); // empty grammar is replaced in UpdateGrammar()
+      base.Encoding = new SymbolicExpressionTreeEncoding(g, 100, 17);
+
+      UpdateGrammar();
+      RegisterEventHandlers();
+    }
+
+
+    public override double Evaluate(ISymbolicExpressionTree tree, IRandom random) {
+      // Doesn't use classes from HeuristicLab.Problems.DataAnalysis.Symbolic to make sure that the implementation can be fully understood easily.
+      // HeuristicLab.Problems.DataAnalysis.Symbolic would already provide all the necessary functionality (esp. interpreter) but at a much higher complexity.
+      // Another argument is that we don't need a reference to HeuristicLab.Problems.DataAnalysis.Symbolic
+
+      var problemData = ProblemData;
+      var rows = ProblemData.TrainingIndices.ToArray();
+      var target = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      var predicted = Interpret(tree, problemData.Dataset, rows);
+
+      OnlineCalculatorError errorState;
+      var r = OnlinePearsonsRCalculator.Calculate(target, predicted, out errorState);
+      if (errorState != OnlineCalculatorError.None) r = 0;
+      return r * r;
+    }
+
+    private IEnumerable<double> Interpret(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
+      // skip programRoot and startSymbol
+      return InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), dataset, rows);
+    }
+
+
+    private IEnumerable<double> InterpretRec(ISymbolicExpressionTreeNode node, IDataset dataset, IEnumerable<int> rows) {
+      var eval = CreateEvalClosure(dataset, rows);
+
+      switch (node.Symbol.Name) {
+        case "+": return eval(node.GetSubtree(0), node.GetSubtree(1), (x, y) => x + y);
+        case "*": return eval(node.GetSubtree(0), node.GetSubtree(1), (x, y) => x * y);
+        case "-": return eval(node.GetSubtree(0), node.GetSubtree(1), (x, y) => x - y);
+        case "%": return eval(node.GetSubtree(0), node.GetSubtree(1), (x, y) => y.IsAlmost(0.0) ? 0.0 : x / y); // protected division
+        default: {
+            double erc;
+            if (double.TryParse(node.Symbol.Name, out erc)) {
+              return rows.Select(_ => erc);
+            } else {
+              // assume that this is a variable name
+              return dataset.GetDoubleValues(node.Symbol.Name, rows);
+            }
+          }
+      }
+    }
+
+    private Func<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode, Func<double, double, double>, IEnumerable<double>> CreateEvalClosure(IDataset dataset, IEnumerable<int> rows) {
+      // capture dataset and rows in scope
+      return (a, b, f) => {
+        var leftResult = InterpretRec(a, dataset, rows);
+        var rightResult = InterpretRec(b, dataset, rows);
+        return leftResult.Zip(rightResult, f);
+      };
+    }
+
+    #region item cloning and persistence
+    // persistence
+    [StorableConstructor]
+    private Problem(bool deserializing) : base(deserializing) { }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      RegisterEventHandlers();
+    }
+
+    // cloning 
+    private Problem(Problem original, Cloner cloner)
+      : base(original, cloner) {
+      RegisterEventHandlers();
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new Problem(this, cloner);
+    }
+    #endregion
+
+    #region events
+
+    private void RegisterEventHandlers() {
+      ProblemDataParameter.ValueChanged += new EventHandler(ProblemDataParameter_ValueChanged);
+      if (ProblemDataParameter.Value != null) ProblemDataParameter.Value.Changed += new EventHandler(ProblemData_Changed);
+    }
+
+    private void ProblemDataParameter_ValueChanged(object sender, EventArgs e) {
+      ProblemDataParameter.Value.Changed += new EventHandler(ProblemData_Changed);
+      OnProblemDataChanged();
+      OnReset();
+    }
+
+    private void ProblemData_Changed(object sender, EventArgs e) {
+      OnReset();
+    }
+
+    private void OnProblemDataChanged() {
+      UpdateGrammar();
+
+      var handler = ProblemDataChanged;
+      if (handler != null) handler(this, EventArgs.Empty);
+    }
+
+    private void UpdateGrammar() {
+      // whenever ProblemData is changed we create a new grammar with the necessary symbols
+      var g = new SimpleSymbolicExpressionGrammar();
+      g.AddSymbols(new[] { "+", "*", "%", "-" }, 2, 2); // % is protected division 1/0 := 0
+
+      foreach (var variableName in ProblemData.AllowedInputVariables)
+        g.AddTerminalSymbol(variableName);
+
+      // generate ephemeral random consts in the range [-10..+10[ (2*number of variables)
+      var rand = new System.Random();
+      for (int i = 0; i < ProblemData.AllowedInputVariables.Count() * 2; i++) {
+        string newErcSy;
+        do {
+          newErcSy = string.Format("{0:F2}", rand.NextDouble() * 20 - 10);
+        } while (g.Symbols.Any(sy => sy.Name == newErcSy)); // it might happen that we generate the same constant twice
+        g.AddTerminalSymbol(newErcSy);
+      }
+
+      Encoding.Grammar = g;
+    }
+
+    #endregion
+
+    #region Import & Export
+    public void Load(IRegressionProblemData data) {
+      Name = data.Name;
+      Description = data.Description;
+      ProblemData = data;
+    }
+
+    public IRegressionProblemData Export() {
+      return ProblemData;
+    }
+    #endregion
+  }
+}
