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source: branches/Persistence Test/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/VarianceAccountedForEvaluator.cs @ 4539

Last change on this file since 4539 was 2222, checked in by gkronber, 15 years ago

Merged changes from GP-refactoring branch back into the trunk #713.

File size: 2.0 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 HeuristicLab.Modeling;
24
25namespace HeuristicLab.GP.StructureIdentification {
26  /// <summary>
27  /// The Variance Accounted For (VAF) function calculates is computed as
28  /// VAF(y,y') = ( 1 - var(y-y')/var(y) )
29  /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
30  /// </summary>
31  public class VarianceAccountedForEvaluator : SimpleGPEvaluatorBase {
32    public override string OutputVariableName {
33      get {
34        return "VAF";
35      }
36    }
37    public override string Description {
38      get {
39        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates
40the variance-accounted-for quality measure for the estimated values vs. the real values of 'TargetVariable'.
41
42The Variance Accounted For (VAF) function is computed as
43VAF(y,y') = ( 1 - var(y-y')/var(y) )
44where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.";
45      }
46    }
47
48    public override double Evaluate(double[,] values) {
49      try { return SimpleVarianceAccountedForEvaluator.Calculate(values); }
50      catch (ArgumentException) {
51        return double.NegativeInfinity;
52      }
53    }
54  }
55}
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