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source: trunk/sources/HeuristicLab.GP.StructureIdentification/Evaluators/MeanSquaredErrorEvaluator.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: 2.7 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;
[702]29using HeuristicLab.DataAnalysis;
[645]30
31namespace HeuristicLab.GP.StructureIdentification {
32  public class MeanSquaredErrorEvaluator : GPEvaluatorBase {
33    public override string Description {
34      get {
35        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
36for the estimated values vs. the real values of 'TargetVariable'.";
37      }
38    }
39
40    public MeanSquaredErrorEvaluator()
41      : base() {
42      AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
43    }
44
[702]45    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
[645]46      double errorsSquaredSum = 0;
[712]47      for (int sample = start; sample < end; sample++) {
48        double original = dataset.GetValue(sample, targetVariable);
[702]49        double estimated = evaluator.Evaluate(sample);
[712]50        if (updateTargetValues) {
51          dataset.SetValue(sample, targetVariable, estimated);
[702]52        }
[712]53        if (!double.IsNaN(original) && !double.IsInfinity(original)) {
[645]54          double error = estimated - original;
55          errorsSquaredSum += error * error;
56        }
57      }
58
59      errorsSquaredSum /= (end - start);
[712]60      if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
[645]61        errorsSquaredSum = double.MaxValue;
62      }
[702]63
64      DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
[712]65      if (mse == null) {
[702]66        mse = new DoubleData();
67        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
68      }
69
[645]70      mse.Data = errorsSquaredSum;
71    }
72  }
73}
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