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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionSolution.cs @ 4401

Last change on this file since 4401 was 4401, checked in by gkronber, 13 years ago

Added model and solution classes for time series prognosis and added views for time series prognosis solutions. #1142

File size: 3.8 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    [StorableConstructor]
38    public SymbolicRegressionSolution(bool deserializing) : base(deserializing) { }
39    public SymbolicRegressionSolution(DataAnalysisProblemData problemData, SymbolicRegressionModel model, double lowerEstimationLimit, double upperEstimationLimit)
40      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
41      this.Model = model;
42    }
43
44    public override Image ItemImage {
45      get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
46    }
47
48    public new SymbolicRegressionModel Model {
49      get { return (SymbolicRegressionModel)base.Model; }
50      set { base.Model = value; }
51    }
52
53    protected override void RecalculateEstimatedValues() {
54      int minLag = 0;
55      var laggedTreeNodes = Model.SymbolicExpressionTree.IterateNodesPrefix().OfType<LaggedVariableTreeNode>();
56      if (laggedTreeNodes.Any())
57        minLag = laggedTreeNodes.Min(node => node.Lag);
58      IEnumerable<double> calculatedValues =
59          from x in Model.GetEstimatedValues(ProblemData, 0 - minLag, ProblemData.Dataset.Rows)
60          let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, x))
61          select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX;
62      estimatedValues = Enumerable.Repeat(double.NaN, Math.Abs(minLag)).Concat(calculatedValues).ToList();
63      OnEstimatedValuesChanged();
64    }
65
66    private List<double> estimatedValues;
67    public override IEnumerable<double> EstimatedValues {
68      get {
69        if (estimatedValues == null) RecalculateEstimatedValues();
70        return estimatedValues.AsEnumerable();
71      }
72    }
73
74    public override IEnumerable<double> EstimatedTrainingValues {
75      get {
76        if (estimatedValues == null) RecalculateEstimatedValues();
77        int start = ProblemData.TrainingSamplesStart.Value;
78        int n = ProblemData.TrainingSamplesEnd.Value - start;
79        return estimatedValues.Skip(start).Take(n).ToList();
80      }
81    }
82
83    public override IEnumerable<double> EstimatedTestValues {
84      get {
85        if (estimatedValues == null) RecalculateEstimatedValues();
86        int start = ProblemData.TestSamplesStart.Value;
87        int n = ProblemData.TestSamplesEnd.Value - start;
88        return estimatedValues.Skip(start).Take(n).ToList();
89      }
90    }
91  }
92}
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