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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs @ 5657

Last change on this file since 5657 was 5657, checked in by gkronber, 12 years ago

#1418 Implemented calculation of thresholds.

File size: 3.8 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 System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Operators;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.Optimization;
32using System;
33
34namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
35  /// <summary>
36  /// Represents a symbolic classification model
37  /// </summary>
38  [StorableClass]
39  [Item(Name = "SymbolicDiscriminantFunctionClassificationModel", Description = "Represents a symbolic classification model unsing a discriminant function.")]
40  public class SymbolicDiscriminantFunctionClassificationModel : SymbolicDataAnalysisModel, ISymbolicDiscriminantFunctionClassificationModel {
41    [Storable]
42    private double[] classValues;
43
44    [Storable]
45    private double[] thresholds;
46    public IEnumerable<double> Thresholds {
47      get { return (IEnumerable<double>)thresholds.Clone(); }
48      set {
49        thresholds = value.ToArray();
50        OnThresholdsChanged(EventArgs.Empty);
51      }
52    }
53
54    [StorableConstructor]
55    protected SymbolicDiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
56    protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner)
57      : base(original, cloner) {
58      classValues = (double[])original.classValues.Clone();
59      thresholds = (double[])original.thresholds.Clone();
60    }
61    public SymbolicDiscriminantFunctionClassificationModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IEnumerable<double> classValues)
62      : base(tree, interpreter) {
63      this.classValues = classValues.ToArray();
64      this.thresholds = new double[0];
65    }
66
67    public override IDeepCloneable Clone(Cloner cloner) {
68      return new SymbolicDiscriminantFunctionClassificationModel(this, cloner);
69    }
70
71    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
72      return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows);
73    }
74
75    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
76      foreach (var x in GetEstimatedValues(dataset, rows)) {
77        int classIndex = 0;
78        // find first threshold value which is smaller than x => class index = threshold index + 1
79        for (int i = 0; i < thresholds.Length; i++) {
80          if (x > thresholds[i]) classIndex++;
81          else break;
82        }
83        yield return classValues.ElementAt(classIndex - 1);
84      }
85    }
86
87    #region events
88    public event EventHandler ThresholdsChanged;
89    protected virtual void OnThresholdsChanged(EventArgs e) {
90      var listener = ThresholdsChanged;
91      if (listener != null) listener(this, e);
92    }
93    #endregion
94  }
95}
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