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source: trunk/sources/HeuristicLab.GP.StructureIdentification/Evaluators/VarianceAccountedForEvaluator.cs @ 1109

Last change on this file since 1109 was 712, checked in by gkronber, 16 years ago

fixed a stupid mistake introduced with r702 #328 (GP evaluation doesn't work in a thread parallel engine).

File size: 3.5 KB
RevLine 
[645]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.Operators;
29using HeuristicLab.DataAnalysis;
30
31namespace HeuristicLab.GP.StructureIdentification {
32  public class VarianceAccountedForEvaluator : GPEvaluatorBase {
33    public override string Description {
34      get {
35        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates
36the variance-accounted-for quality measure for the estimated values vs. the real values of 'TargetVariable'.
37
38The Variance Accounted For (VAF) function is computed as
39VAF(y,y') = ( 1 - var(y-y')/var(y) )
40where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.";
41      }
42    }
43
44    /// <summary>
45    /// The Variance Accounted For (VAF) function calculates is computed as
46    /// VAF(y,y') = ( 1 - var(y-y')/var(y) )
47    /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
48    /// </summary>
49    public VarianceAccountedForEvaluator()
50      : base() {
51      AddVariableInfo(new VariableInfo("VAF", "The variance-accounted-for quality of the model", typeof(DoubleData), VariableKind.New));
52
53    }
54
[702]55    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
[645]56      int nSamples = end - start;
57      double[] errors = new double[nSamples];
58      double[] originalTargetVariableValues = new double[nSamples];
[712]59      for (int sample = start; sample < end; sample++) {
[702]60        double estimated = evaluator.Evaluate(sample);
[712]61        double original = dataset.GetValue(sample, targetVariable);
62        if (updateTargetValues) {
63          dataset.SetValue(sample, targetVariable, estimated);
[702]64        }
[712]65        if (!double.IsNaN(original) && !double.IsInfinity(original)) {
[645]66          errors[sample - start] = original - estimated;
67          originalTargetVariableValues[sample - start] = original;
68        }
69      }
70      double errorsVariance = Statistics.Variance(errors);
71      double originalsVariance = Statistics.Variance(originalTargetVariableValues);
72      double quality = 1 - errorsVariance / originalsVariance;
73
[712]74      if (double.IsNaN(quality) || double.IsInfinity(quality)) {
[645]75        quality = double.MaxValue;
76      }
[702]77      DoubleData vaf = GetVariableValue<DoubleData>("VAF", scope, false, false);
[712]78      if (vaf == null) {
[702]79        vaf = new DoubleData();
80        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("VAF"), vaf));
81      }
82
[645]83      vaf.Data = quality;
84    }
85  }
86}
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