source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs @ 7259

Last change on this file since 7259 was 7259, checked in by swagner, 10 years ago

Updated year of copyrights to 2012 (#1716)

File size: 5.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis {
29  /// <summary>
30  /// Represents a classification solution that uses a discriminant function and classification thresholds.
31  /// </summary>
32  [StorableClass]
33  [Item("DiscriminantFunctionClassificationSolution", "Represents a classification solution that uses a discriminant function and classification thresholds.")]
34  public abstract class DiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolutionBase {
35    protected readonly Dictionary<int, double> valueEvaluationCache;
36    protected readonly Dictionary<int, double> classValueEvaluationCache;
37
38    [StorableConstructor]
39    protected DiscriminantFunctionClassificationSolution(bool deserializing)
40      : base(deserializing) {
41      valueEvaluationCache = new Dictionary<int, double>();
42      classValueEvaluationCache = new Dictionary<int, double>();
43    }
44    protected DiscriminantFunctionClassificationSolution(DiscriminantFunctionClassificationSolution original, Cloner cloner)
45      : base(original, cloner) {
46      valueEvaluationCache = new Dictionary<int, double>(original.valueEvaluationCache);
47      classValueEvaluationCache = new Dictionary<int, double>(original.classValueEvaluationCache);
48    }
49    protected DiscriminantFunctionClassificationSolution(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
50      : base(model, problemData) {
51      valueEvaluationCache = new Dictionary<int, double>();
52      classValueEvaluationCache = new Dictionary<int, double>();
53
54      SetAccuracyMaximizingThresholds();
55    }
56
57    public override IEnumerable<double> EstimatedClassValues {
58      get { return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
59    }
60    public override IEnumerable<double> EstimatedTrainingClassValues {
61      get { return GetEstimatedClassValues(ProblemData.TrainingIndizes); }
62    }
63    public override IEnumerable<double> EstimatedTestClassValues {
64      get { return GetEstimatedClassValues(ProblemData.TestIndizes); }
65    }
66
67    public override IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) {
68      var rowsToEvaluate = rows.Except(classValueEvaluationCache.Keys);
69      var rowsEnumerator = rowsToEvaluate.GetEnumerator();
70      var valuesEnumerator = Model.GetEstimatedClassValues(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
71
72      while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
73        classValueEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
74      }
75
76      return rows.Select(row => classValueEvaluationCache[row]);
77    }
78
79
80    public override IEnumerable<double> EstimatedValues {
81      get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
82    }
83    public override IEnumerable<double> EstimatedTrainingValues {
84      get { return GetEstimatedValues(ProblemData.TrainingIndizes); }
85    }
86    public override IEnumerable<double> EstimatedTestValues {
87      get { return GetEstimatedValues(ProblemData.TestIndizes); }
88    }
89
90    public override IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
91      var rowsToEvaluate = rows.Except(valueEvaluationCache.Keys);
92      var rowsEnumerator = rowsToEvaluate.GetEnumerator();
93      var valuesEnumerator = Model.GetEstimatedValues(ProblemData.Dataset, rowsToEvaluate).GetEnumerator();
94
95      while (rowsEnumerator.MoveNext() & valuesEnumerator.MoveNext()) {
96        valueEvaluationCache.Add(rowsEnumerator.Current, valuesEnumerator.Current);
97      }
98
99      return rows.Select(row => valueEvaluationCache[row]);
100    }
101
102    protected override void OnModelChanged() {
103      valueEvaluationCache.Clear();
104      classValueEvaluationCache.Clear();
105      base.OnModelChanged();
106    }
107    protected override void OnModelThresholdsChanged(System.EventArgs e) {
108      classValueEvaluationCache.Clear();
109      base.OnModelThresholdsChanged(e);
110    }
111    protected override void OnProblemDataChanged() {
112      valueEvaluationCache.Clear();
113      classValueEvaluationCache.Clear();
114      base.OnProblemDataChanged();
115    }
116  }
117}
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