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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationSolution.cs @ 7234

Last change on this file since 7234 was 7234, checked in by gkronber, 11 years ago

#1685: changed simplification view for symbolic classification solutions to use the Gini index to determine the impact of a node it describes the degree of separation of the classes and we do not have to search for the optimal threshold value each time we calculate the impact of one node. Also fixed a problem with the Gini index result of classification solutions using a discriminating function as for these solutions the Gini index is calculated twice (once for the class values and once for the output values of the discriminating function.)

File size: 3.7 KB
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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}
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