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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleVarianceAccountedForEvaluator.cs @ 3999

Last change on this file since 3999 was 3452, checked in by gkronber, 15 years ago

Included tracking of best of run solution (based on validation set) and calculation of MSE, R² and rel. Error on training and test sets. #938 (Data types and operators for regression problems)

File size: 3.4 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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.Parameters;
30
31namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
32  /// <summary>
33  /// The Variance Accounted For (VAF) function calculates is computed as
34  /// VAF(y,y') =  1 - var(y-y')/var(y)
35  /// where y' denotes the predicted / modelled values for y and var(x) the variance of a signal x.
36  /// </summary>
37  public class SimpleVarianceAccountedForEvaluator : SimpleEvaluator {
38
39    public ILookupParameter<DoubleValue> VarianceAccountedForParameter {
40      get { return (ILookupParameter<DoubleValue>)Parameters["VarianceAccountedFor"]; }
41    }
42
43    public SimpleVarianceAccountedForEvaluator() {
44      Parameters.Add(new LookupParameter<DoubleValue>("VarianceAccountedFor", "The variance of the original values accounted for by the estimated values (VAF(y,y') = 1 - var(y-y') / var(y) )."));
45    }
46
47    protected override void Apply(DoubleMatrix values) {
48      var original = from i in Enumerable.Range(0, values.Rows)
49                     select values[i, ORIGINAL_INDEX];
50      var estimated = from i in Enumerable.Range(0, values.Rows)
51                      select values[i, ESTIMATION_INDEX];
52      VarianceAccountedForParameter.ActualValue = new DoubleValue(Calculate(original, estimated));
53    }
54
55    public static double Calculate(IEnumerable<double> original, IEnumerable<double> estimated) {
56      var originalEnumerator = original.GetEnumerator();
57      var estimatedEnumerator = estimated.GetEnumerator();
58      var errors = new List<double>();
59      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
60        double e = estimatedEnumerator.Current;
61        double o = originalEnumerator.Current;
62        if (!double.IsNaN(e) && !double.IsInfinity(e) &&
63          !double.IsNaN(o) && !double.IsInfinity(o)) {
64          errors.Add(o - e);
65        }
66      }
67      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
68        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
69      }
70
71      double errorsVariance = errors.Variance();
72      double originalsVariance = original.Variance();
73      if (originalsVariance.IsAlmost(0.0))
74        if (errorsVariance.IsAlmost(0.0)) {
75          return 1.0;
76        } else {
77          return double.MaxValue;
78        } else {
79        return 1.0 - errorsVariance / originalsVariance;
80      }
81    }
82  }
83}
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