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source: trunk/sources/HeuristicLab.Modeling/3.2/SimpleMeanAbsolutePercentageOfRangeErrorEvaluator.cs @ 2808

Last change on this file since 2808 was 2357, checked in by gkronber, 15 years ago

Fixed #740 (SimpleEvaluators in HL.GP.StructId and HL.SVM are not compatible with evaluators in HL.Modeling).

File size: 2.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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 System.Collections.Generic;
24using System.Linq;
25using System.Text;
26using HeuristicLab.Core;
27using HeuristicLab.Common;
28using HeuristicLab.Data;
29using HeuristicLab.DataAnalysis;
30
31namespace HeuristicLab.Modeling {
32  public class SimpleMeanAbsolutePercentageOfRangeErrorEvaluator : SimpleEvaluatorBase {
33    public override string OutputVariableName {
34      get {
35        return "MAPRE";
36      }
37    }
38
39    public override double Evaluate(double[,] values) {
40      try {
41        return Calculate(values);
42      }
43      catch (ArgumentException) {
44        return double.PositiveInfinity;
45      }
46    }
47
48    public static double Calculate(double[,] values) {
49      double errorsSum = 0.0;
50      int n = 0;
51      // copy to one-dimensional array for range calculation
52      double[] originalValues = new double[values.GetLength(0)];
53      for (int i = 0; i < originalValues.Length; i++) originalValues[i] = values[i, ORIGINAL_INDEX];
54      double range = Statistics.Range(originalValues);
55      if (double.IsInfinity(range)) throw new ArgumentException("Range of elements in values is infinity");
56      if (range.IsAlmost(0.0)) throw new ArgumentException("Range of elements in values is zero");
57
58      for (int i = 0; i < values.GetLength(0); i++) {
59        double estimated = values[i, ESTIMATION_INDEX];
60        double original = values[i, ORIGINAL_INDEX];
61
62        if (!double.IsNaN(estimated) && !double.IsInfinity(estimated) &&
63          !double.IsNaN(original) && !double.IsInfinity(original) && original != 0.0) {
64          double percent_error = Math.Abs((estimated - original) / range);
65          errorsSum += percent_error;
66          n++;
67        }
68      }
69      if (double.IsInfinity(range) || n == 0) {
70        throw new ArgumentException("Mean of absolute percentage of range error is not defined for input vectors of NaN or Inf");
71      } else {
72        return errorsSum / n;
73      }
74    }
75  }
76}
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