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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineAccuracyEvaluator.cs @ 5649

Last change on this file since 5649 was 5649, checked in by gkronber, 12 years ago

#1418 Implemented classes for classification based on a discriminant function and thresholds and implemented interfaces and base classes for clustering.

File size: 2.9 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;
23using HeuristicLab.Common;
24using System.Collections.Generic;
25
26namespace HeuristicLab.Problems.DataAnalysis {
27  public class OnlineAccuracyEvaluator : IOnlineEvaluator {
28
29    private int correctlyClassified;
30    private int n;
31    public double Accuracy {
32      get {
33        if (n < 1)
34          throw new InvalidOperationException("No elements");
35        else
36          return correctlyClassified / n;
37      }
38    }
39
40    public OnlineAccuracyEvaluator() {
41      Reset();
42    }
43
44    #region IOnlineEvaluator Members
45    public double Value {
46      get { return Accuracy; }
47    }
48    public void Reset() {
49      n = 0;
50      correctlyClassified = 0;
51    }
52
53    public void Add(double original, double estimated) {
54      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
55          double.IsNaN(original) || double.IsInfinity(original)) {
56        throw new ArgumentException("Accuracy is not defined for NaN or infinity elements");
57      } else {
58        if (original.IsAlmost(estimated)) correctlyClassified++;
59        n++;
60      }
61    }
62    #endregion
63
64    public static double Calculate(IEnumerable<double> first, IEnumerable<double> second) {
65      IEnumerator<double> firstEnumerator = first.GetEnumerator();
66      IEnumerator<double> secondEnumerator = second.GetEnumerator();
67      OnlineAccuracyEvaluator accuracyEvaluator = new OnlineAccuracyEvaluator();
68
69      // always move forward both enumerators (do not use short-circuit evaluation!)
70      while (firstEnumerator.MoveNext() & secondEnumerator.MoveNext()) {
71        double estimated = secondEnumerator.Current;
72        double original = firstEnumerator.Current;
73        accuracyEvaluator.Add(original, estimated);
74      }
75
76      // check if both enumerators are at the end to make sure both enumerations have the same length
77      if (secondEnumerator.MoveNext() || firstEnumerator.MoveNext()) {
78        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
79      } else {
80        return accuracyEvaluator.Accuracy;
81      }
82    }
83  }
84}
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