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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationSolution.cs @ 6589

Last change on this file since 6589 was 6589, checked in by mkommend, 11 years ago

#1600: Adapted classification solutions to the same design as used by regression solutions.

File size: 3.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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 = "SymbolicDiscriminantFunctionClassificationSolution", Description = "Represents a symbolic classification solution (model + data) and attributes of the solution like accuracy and complexity.")]
34  public sealed class SymbolicDiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolution, ISymbolicClassificationSolution {
35    private const string ModelLengthResultName = "Model Length";
36    private const string ModelDepthResultName = "Model Depth";
37
38    public new ISymbolicDiscriminantFunctionClassificationModel Model {
39      get { return (ISymbolicDiscriminantFunctionClassificationModel)base.Model; }
40      set { base.Model = value; }
41    }
42
43    ISymbolicClassificationModel ISymbolicClassificationSolution.Model {
44      get { return Model; }
45    }
46
47    ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
48      get { return Model; }
49    }
50    public int ModelLength {
51      get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
52      private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
53    }
54
55    public int ModelDepth {
56      get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
57      private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
58    }
59    [StorableConstructor]
60    private SymbolicDiscriminantFunctionClassificationSolution(bool deserializing) : base(deserializing) { }
61    private SymbolicDiscriminantFunctionClassificationSolution(SymbolicDiscriminantFunctionClassificationSolution original, Cloner cloner)
62      : base(original, cloner) {
63    }
64    public SymbolicDiscriminantFunctionClassificationSolution(ISymbolicDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
65      : base(model, problemData) {
66      Add(new Result(ModelLengthResultName, "Length of the symbolic classification model.", new IntValue()));
67      Add(new Result(ModelDepthResultName, "Depth of the symbolic classification model.", new IntValue()));
68      RecalculateResults();
69    }
70
71    public override IDeepCloneable Clone(Cloner cloner) {
72      return new SymbolicDiscriminantFunctionClassificationSolution(this, cloner);
73    }
74
75    protected override void RecalculateResults() {
76      CalculateResults();
77      CalculateRegressionResults();
78      ModelLength = Model.SymbolicExpressionTree.Length;
79      ModelDepth = Model.SymbolicExpressionTree.Depth;
80    }
81
82  }
83}
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