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source: branches/ParameterBinding/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/OnlineNormalizedMeanSquaredErrorEvaluator.cs @ 12726

Last change on this file since 12726 was 4068, checked in by swagner, 14 years ago

Sorted usings and removed unused usings in entire solution (#1094)

File size: 2.3 KB
Line 
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;
23
24namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
25  public class OnlineNormalizedMeanSquaredErrorEvaluator : IOnlineEvaluator {
26    private OnlineMeanAndVarianceCalculator meanSquaredErrorCalculator;
27    private OnlineMeanAndVarianceCalculator originalVarianceCalculator;
28
29    public double NormalizedMeanSquaredError {
30      get {
31        return meanSquaredErrorCalculator.Mean / originalVarianceCalculator.Variance;
32      }
33    }
34
35    public OnlineNormalizedMeanSquaredErrorEvaluator() {
36      meanSquaredErrorCalculator = new OnlineMeanAndVarianceCalculator();
37      originalVarianceCalculator = new OnlineMeanAndVarianceCalculator();
38      Reset();
39    }
40
41    #region IOnlineEvaluator Members
42    public double Value {
43      get { return NormalizedMeanSquaredError; }
44    }
45
46    public void Reset() {
47      meanSquaredErrorCalculator.Reset();
48      originalVarianceCalculator.Reset();
49    }
50
51    public void Add(double original, double estimated) {
52      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
53          double.IsNaN(original) || double.IsInfinity(original)) {
54        throw new ArgumentException("Mean squared error is not defined for NaN or infinity elements");
55      } else {
56        double error = estimated - original;
57        meanSquaredErrorCalculator.Add(error * error);
58        originalVarianceCalculator.Add(original);
59      }
60    }
61    #endregion
62  }
63}
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