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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis/3.4/DiscriminantFunctionClassificationModel.cs @ 5697

Last change on this file since 5697 was 5681, checked in by gkronber, 14 years ago

#1418 refactored threshold calculators.

File size: 4.2 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.Operators;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Optimization;
31using System;
32
33namespace HeuristicLab.Problems.DataAnalysis {
34  /// <summary>
35  /// Represents discriminant function classification data analysis models.
36  /// </summary>
37  [StorableClass]
38  [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
39  public class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
40    [Storable]
41    private IRegressionModel model;
42    [Storable]
43    private double[] classValues;
44    // class values are not necessarily sorted in ascending order
45    public IEnumerable<double> ClassValues {
46      get { return (double[])classValues.Clone(); }
47      set {
48        if (value == null) throw new ArgumentException();
49        double[] newValue = value.ToArray();
50        if (newValue.Length != classValues.Length) throw new ArgumentException();
51        classValues = newValue;
52      }
53    }
54    [Storable]
55    private double[] thresholds;
56    public IEnumerable<double> Thresholds {
57      get { return (IEnumerable<double>)thresholds.Clone(); }
58      set {
59        thresholds = value.ToArray();
60        OnThresholdsChanged(EventArgs.Empty);
61      }
62    }
63
64
65    [StorableConstructor]
66    protected DiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
67    protected DiscriminantFunctionClassificationModel(DiscriminantFunctionClassificationModel original, Cloner cloner)
68      : base(original, cloner) {
69      model = cloner.Clone(original.model);
70      classValues = (double[])original.classValues.Clone();
71      thresholds = (double[])original.thresholds.Clone();
72    }
73    public DiscriminantFunctionClassificationModel(IRegressionModel model, IEnumerable<double> classValues, IEnumerable<double> thresholds)
74      : base() {
75      this.name = ItemName;
76      this.description = ItemDescription;
77      this.model = model;
78      this.classValues = classValues.ToArray();
79      this.thresholds = thresholds.ToArray();
80    }
81
82    public override IDeepCloneable Clone(Cloner cloner) {
83      return new DiscriminantFunctionClassificationModel(this, cloner);
84    }
85
86    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
87      return model.GetEstimatedValues(dataset, rows);
88    }
89
90    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
91      foreach (var x in GetEstimatedValues(dataset, rows)) {
92        int classIndex = 0;
93        // find first threshold value which is larger than x => class index = threshold index + 1
94        for (int i = 0; i < thresholds.Length; i++) {
95          if (x > thresholds[i]) classIndex++;
96          else break;
97        }
98        yield return classValues.ElementAt(classIndex);
99      }
100    }
101    #region events
102    public event EventHandler ThresholdsChanged;
103    protected virtual void OnThresholdsChanged(EventArgs e) {
104      var listener = ThresholdsChanged;
105      if (listener != null) listener(this, e);
106    }
107    #endregion
108  }
109}
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