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source:trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/OnlineMeanAndVarianceCalculator.cs@4022

Last change on this file since 4022 was 4022, checked in by gkronber, 14 years ago

Worked on symbolic regression classes to prepare for time series prognosis plugin. #1081

File size: 2.1 KB
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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
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.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Parameters;
30
31namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
32  public class OnlineMeanAndVarianceCalculator {
33
34    private double m_oldM, m_newM, m_oldS, m_newS;
35    private int n;
36
37    public double Variance {
38      get {
39        return (n > 1) ? m_newS / (n - 1) : 0.0;
40      }
41    }
42
43    public double Mean {
44      get {
45        return (n > 0) ? m_newM : 0.0;
46      }
47    }
48
49    public OnlineMeanAndVarianceCalculator() {
50      Reset();
51    }
52
53    public void Reset() {
54      n = 0;
55    }
56
57    public void Add(double x) {
58      if (double.IsNaN(x) || double.IsInfinity(x)) {
59        throw new ArgumentException("Mean and variance are not defined for NaN or infinity elements");
60      } else {
61        n++;
62        // See Knuth TAOCP vol 2, 3rd edition, page 232
63        if (n == 1) {
64          m_oldM = m_newM = x;
65          m_oldS = 0.0;
66        } else {
67          m_newM = m_oldM + (x - m_oldM) / n;
68          m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
69
70          // set up for next iteration
71          m_oldM = m_newM;
72          m_oldS = m_newS;
73        }
74      }
75    }
76  }
77}
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