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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineEvaluators/OnlineMeanAndVarianceCalculator.cs @ 5904

Last change on this file since 5904 was 5904, checked in by gkronber, 13 years ago

#1453: improved performance of online evaluators.

File size: 3.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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;
24
25namespace HeuristicLab.Problems.DataAnalysis {
26  public class OnlineMeanAndVarianceCalculator {
27
28    private double m_oldM, m_newM, m_oldS, m_newS;
29    private int n;
30
31    private OnlineEvaluatorError varianceErrorState;
32    public OnlineEvaluatorError VarianceErrorState {
33      get { return varianceErrorState; }
34    }
35
36    public double Variance {
37      get {
38        return (n > 1) ? m_newS / (n - 1) : 0.0;
39      }
40    }
41
42    private OnlineEvaluatorError errorState;
43    public OnlineEvaluatorError PopulationVarianceErrorState {
44      get { return errorState; }
45    }
46    public double PopulationVariance {
47      get {
48        return (n > 0) ? m_newS / n : 0.0;
49      }
50    }
51
52    public OnlineEvaluatorError MeanErrorState {
53      get { return errorState; }
54    }
55    public double Mean {
56      get {
57        return (n > 0) ? m_newM : 0.0;
58      }
59    }
60
61    public int Count {
62      get { return n; }
63    }
64
65    public OnlineMeanAndVarianceCalculator() {
66      Reset();
67    }
68
69    public void Reset() {
70      n = 0;
71      errorState = OnlineEvaluatorError.InsufficientElementsAdded;
72      varianceErrorState = OnlineEvaluatorError.InsufficientElementsAdded;
73    }
74
75    public void Add(double x) {
76      if (double.IsNaN(x) || double.IsInfinity(x) || (errorState & OnlineEvaluatorError.InvalidValueAdded) > 0) {
77        errorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
78        varianceErrorState = errorState | OnlineEvaluatorError.InvalidValueAdded;
79      } else {
80        n++;
81        // See Knuth TAOCP vol 2, 3rd edition, page 232
82        if (n == 1) {
83          m_oldM = m_newM = x;
84          m_oldS = 0.0;
85          errorState = errorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 1
86        } else {
87          varianceErrorState = varianceErrorState & (~OnlineEvaluatorError.InsufficientElementsAdded);        // n >= 2
88          m_newM = m_oldM + (x - m_oldM) / n;
89          m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
90
91          // set up for next iteration
92          m_oldM = m_newM;
93          m_oldS = m_newS;
94        }
95      }
96    }
97
98    public static void Calculate(IEnumerable<double> x, out double mean, out double variance, out OnlineEvaluatorError meanErrorState, out OnlineEvaluatorError varianceErrorState) {
99      OnlineMeanAndVarianceCalculator meanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
100      foreach (double xi in x) {
101        meanAndVarianceCalculator.Add(xi);
102      }
103      mean = meanAndVarianceCalculator.Mean;
104      variance = meanAndVarianceCalculator.Variance;
105      meanErrorState = meanAndVarianceCalculator.MeanErrorState;
106      varianceErrorState = meanAndVarianceCalculator.VarianceErrorState;
107    }
108  }
109}
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