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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationSolution.cs @ 10771

Last change on this file since 10771 was 9456, checked in by swagner, 12 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 3.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Optimization;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
29  /// <summary>
30  /// Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity
31  /// </summary>
32  [StorableClass]
33  [Item(Name = "SymbolicClassificationSolution", Description = "Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity.")]
34  public sealed class SymbolicClassificationSolution : ClassificationSolution, ISymbolicClassificationSolution {
35    private const string ModelLengthResultName = "ModelLength";
36    private const string ModelDepthResultName = "ModelDepth";
37
38    public new ISymbolicClassificationModel Model {
39      get { return (ISymbolicClassificationModel)base.Model; }
40      set { base.Model = value; }
41    }
42
43    ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
44      get { return (ISymbolicDataAnalysisModel)base.Model; }
45    }
46    public int ModelLength {
47      get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
48      private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
49    }
50
51    public int ModelDepth {
52      get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
53      private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
54    }
55
56    [StorableConstructor]
57    private SymbolicClassificationSolution(bool deserializing) : base(deserializing) { }
58    private SymbolicClassificationSolution(SymbolicClassificationSolution original, Cloner cloner)
59      : base(original, cloner) {
60    }
61    public SymbolicClassificationSolution(ISymbolicClassificationModel model, IClassificationProblemData problemData)
62      : base(model, problemData) {
63      Add(new Result(ModelLengthResultName, "Length of the symbolic classification model.", new IntValue()));
64      Add(new Result(ModelDepthResultName, "Depth of the symbolic classification model.", new IntValue()));
65      RecalculateResults();
66    }
67
68    public override IDeepCloneable Clone(Cloner cloner) {
69      return new SymbolicClassificationSolution(this, cloner);
70    }
71
72    protected override void RecalculateResults() {
73      base.RecalculateResults();
74      ModelLength = Model.SymbolicExpressionTree.Length;
75      ModelDepth = Model.SymbolicExpressionTree.Depth;
76    }
77  }
78}
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