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source: branches/FunctionsAndStructIdRefactoring/HeuristicLab.StructureIdentification/Evaluation/MeanSquaredErrorEvaluator.cs @ 143

Last change on this file since 143 was 143, checked in by gkronber, 17 years ago

worked on #112 in the refactoring branch

File size: 2.6 KB
RevLine 
[2]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 {
[128]33  public class MeanSquaredErrorEvaluator : GPEvaluatorBase {
[2]34    public override string Description {
[128]35      get {
[142]36        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
[128]37for the estimated values vs. the real values of 'TargetVariable'.";
38      }
[2]39    }
40
41    public MeanSquaredErrorEvaluator()
42      : base() {
43    }
44
[142]45    public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
[2]46      double errorsSquaredSum = 0;
[128]47      double targetMean = dataset.GetMean(targetVariable);
48      for(int sample = 0; sample < dataset.Rows; sample++) {
[143]49
[142]50        double estimated = functionTree.Evaluate(dataset, sample);
[2]51        double original = dataset.GetValue(sample, targetVariable);
52        if(double.IsNaN(estimated) || double.IsInfinity(estimated)) {
53          estimated = targetMean + maximumPunishment;
54        } else if(estimated > targetMean + maximumPunishment) {
55          estimated = targetMean + maximumPunishment;
56        } else if(estimated < targetMean - maximumPunishment) {
57          estimated = targetMean - maximumPunishment;
58        }
59        double error = estimated - original;
60        errorsSquaredSum += error * error;
61      }
[128]62      errorsSquaredSum /= dataset.Rows;
[2]63      if(double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
64        errorsSquaredSum = double.MaxValue;
65      }
[128]66      return errorsSquaredSum;
[2]67    }
68  }
69}
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