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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionSolution.cs @ 4443

Last change on this file since 4443 was 4443, checked in by gkronber, 13 years ago

Added default constructor for SymbolicRegressionSolution. #1163

File size: 3.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Drawing;
25using System.Linq;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
29
30namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
31  /// <summary>
32  /// Represents a solution for a symbolic regression problem which can be visualized in the GUI.
33  /// </summary>
34  [Item("SymbolicRegressionSolution", "Represents a solution for a symbolic regression problem which can be visualized in the GUI.")]
35  [StorableClass]
36  public sealed class SymbolicRegressionSolution : DataAnalysisSolution {
37    public SymbolicRegressionSolution() : base() { } // for cloning
38    [StorableConstructor]
39    public SymbolicRegressionSolution(bool deserializing) : base(deserializing) { }
40    public SymbolicRegressionSolution(DataAnalysisProblemData problemData, SymbolicRegressionModel model, double lowerEstimationLimit, double upperEstimationLimit)
41      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
42      this.Model = model;
43    }
44
45    public override Image ItemImage {
46      get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
47    }
48
49    public new SymbolicRegressionModel Model {
50      get { return (SymbolicRegressionModel)base.Model; }
51      set { base.Model = value; }
52    }
53
54    protected override void RecalculateEstimatedValues() {
55      int minLag = 0;
56      var laggedTreeNodes = Model.SymbolicExpressionTree.IterateNodesPrefix().OfType<LaggedVariableTreeNode>();
57      if (laggedTreeNodes.Any())
58        minLag = laggedTreeNodes.Min(node => node.Lag);
59      IEnumerable<double> calculatedValues =
60          from x in Model.GetEstimatedValues(ProblemData, 0 - minLag, ProblemData.Dataset.Rows)
61          let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, x))
62          select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX;
63      estimatedValues = Enumerable.Repeat(double.NaN, Math.Abs(minLag)).Concat(calculatedValues).ToList();
64      OnEstimatedValuesChanged();
65    }
66
67    private List<double> estimatedValues;
68    public override IEnumerable<double> EstimatedValues {
69      get {
70        if (estimatedValues == null) RecalculateEstimatedValues();
71        return estimatedValues.AsEnumerable();
72      }
73    }
74
75    public override IEnumerable<double> EstimatedTrainingValues {
76      get {
77        if (estimatedValues == null) RecalculateEstimatedValues();
78        int start = ProblemData.TrainingSamplesStart.Value;
79        int n = ProblemData.TrainingSamplesEnd.Value - start;
80        return estimatedValues.Skip(start).Take(n).ToList();
81      }
82    }
83
84    public override IEnumerable<double> EstimatedTestValues {
85      get {
86        if (estimatedValues == null) RecalculateEstimatedValues();
87        int start = ProblemData.TestSamplesStart.Value;
88        int n = ProblemData.TestSamplesEnd.Value - start;
89        return estimatedValues.Skip(start).Take(n).ToList();
90      }
91    }
92  }
93}
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