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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAndVarianceCalculator.cs @ 11422

Last change on this file since 11422 was 11422, checked in by mkommend, 8 years ago

#2255: Reverted r11417 due to broken unit tests.

File size: 3.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 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.Collections.Generic;
23
24namespace HeuristicLab.Problems.DataAnalysis {
25  public class OnlineMeanAndVarianceCalculator {
26
27    private double m_oldM, m_newM, m_oldS, m_newS;
28    private int n;
29
30    private OnlineCalculatorError varianceErrorState;
31    public OnlineCalculatorError VarianceErrorState {
32      get { return varianceErrorState; }
33    }
34
35    public double Variance {
36      get {
37        return (n > 1) ? m_newS / (n - 1) : 0.0;
38      }
39    }
40
41    private OnlineCalculatorError errorState;
42    public OnlineCalculatorError PopulationVarianceErrorState {
43      get { return errorState; }
44    }
45    public double PopulationVariance {
46      get {
47        return (n > 0) ? m_newS / n : 0.0;
48      }
49    }
50
51    public OnlineCalculatorError MeanErrorState {
52      get { return errorState; }
53    }
54    public double Mean {
55      get {
56        return (n > 0) ? m_newM : 0.0;
57      }
58    }
59
60    public int Count {
61      get { return n; }
62    }
63
64    public OnlineMeanAndVarianceCalculator() {
65      Reset();
66    }
67
68    public void Reset() {
69      n = 0;
70      errorState = OnlineCalculatorError.InsufficientElementsAdded;
71      varianceErrorState = OnlineCalculatorError.InsufficientElementsAdded;
72    }
73
74    public void Add(double x) {
75      if (double.IsNaN(x) || double.IsInfinity(x) || x > 1E13 || x < -1E13 || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
76        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
77        varianceErrorState = errorState | OnlineCalculatorError.InvalidValueAdded;
78      } else {
79        n++;
80        // See Knuth TAOCP vol 2, 3rd edition, page 232
81        if (n == 1) {
82          m_oldM = m_newM = x;
83          m_oldS = 0.0;
84          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
85        } else {
86
87          varianceErrorState = varianceErrorState & (~OnlineCalculatorError.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 OnlineCalculatorError meanErrorState, out OnlineCalculatorError 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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