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source: branches/HL-3.2-MonoMigration/HeuristicLab.StructureIdentification/Evaluation/EarlyStoppingMeanSquaredErrorEvaluator.cs @ 3747

Last change on this file since 3747 was 482, checked in by gkronber, 16 years ago

made a few more improvements in the GP evaluators (ticket #242 All GP evaluators should support the 'UseEstimatedTargetValues' switch for autoregressive modelling)

File size: 3.1 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.Operators;
29using HeuristicLab.Functions;
30using HeuristicLab.DataAnalysis;
31
32namespace HeuristicLab.StructureIdentification {
33  public class EarlyStoppingMeanSquaredErrorEvaluator : MeanSquaredErrorEvaluator {
34    private double qualityLimit;
35    public override string Description {
36      get {
37        return @"Evaluates 'FunctionTree' for all samples of the dataset and calculates the mean-squared-error
38for the estimated values vs. the real values of 'TargetVariable'.
39This operator stops the computation as soon as an upper limit for the mean-squared-error is reached.";
40      }
41    }
42
43    public EarlyStoppingMeanSquaredErrorEvaluator()
44      : base() {
45      AddVariableInfo(new VariableInfo("QualityLimit", "The upper limit of the MSE which is used as early stopping criterion.", typeof(DoubleData), VariableKind.In));
46    }
47
48    public override IOperation Apply(IScope scope) {
49      qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, false).Data;
50      return base.Apply(scope);
51    }
52
53    // evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
54    public override void Evaluate(int start, int end) {
55      double errorsSquaredSum = 0;
56      int rows = end - start;
57      for(int sample = start; sample < end; sample++) {
58        double estimated = GetEstimatedValue(sample);
59        double original = GetOriginalValue(sample);
60        SetOriginalValue(sample, estimated);
61        if(!double.IsNaN(original) && !double.IsInfinity(original)) {
62          double error = estimated - original;
63          errorsSquaredSum += error * error;
64        }
65        // check the limit and stop as soon as we hit the limit
66        if(errorsSquaredSum / rows >= qualityLimit) {
67          mse.Data = errorsSquaredSum / (sample - start + 1); // return estimated MSE (when the remaining errors are on average the same)
68          return;
69        }
70      }
71      errorsSquaredSum /= rows;
72      if(double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
73        errorsSquaredSum = double.MaxValue;
74      }
75      mse.Data = errorsSquaredSum;
76    }
77  }
78}
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