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source: branches/HeuristicLab.Hive_Milestone3/sources/HeuristicLab.Modeling/3.3/SimpleVarianceAccountedForEvaluator.cs @ 4593

Last change on this file since 4593 was 1888, checked in by gkronber, 16 years ago

Added simple evaluators for mean absolute percentage error, mean absolute percentage of range error and, variance accounted for. #635 (Plugin HeuristicLab.Modeling as a common basis for all data-based modeling algorithms)

File size: 2.5 KB
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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.Data;
28using HeuristicLab.DataAnalysis;
29
30namespace HeuristicLab.Modeling {
31  /// <summary>
32  /// The Variance Accounted For (VAF) function calculates is computed as
33  /// VAF(y,y') = ( 1 - var(y-y')/var(y) )
34  /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
35  /// </summary>
36  public class SimpleVarianceAccountedForEvaluator : SimpleEvaluatorBase {
37
38    public override string OutputVariableName {
39      get {
40        return "VAF";
41      }
42    }
43
44    public override double Evaluate(double[,] values) {
45      return Calculate(values);
46    }
47
48    public static double Calculate(double[,] values) {
49      int n = values.GetLength(0);
50      double[] errors = new double[n];
51      double[] originalTargetVariableValues = new double[n];
52      for (int i = 0; i < n; i++) {
53        double estimated = values[i, 0];
54        double original = values[i, 1];
55        if (!double.IsNaN(estimated) && !double.IsInfinity(estimated) &&
56          !double.IsNaN(original) && !double.IsInfinity(original)) {
57          errors[i] = original - estimated;
58          originalTargetVariableValues[i] = original;
59        } else {
60          errors[i] = double.NaN;
61          originalTargetVariableValues[i] = double.NaN;
62        }
63      }
64      double errorsVariance = Statistics.Variance(errors);
65      double originalsVariance = Statistics.Variance(originalTargetVariableValues);
66      double quality = 1 - errorsVariance / originalsVariance;
67
68      return quality;
69    }
70  }
71}
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