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source: branches/3116_GAM_Interactions/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAndVarianceCalculator.cs @ 18236

Last change on this file since 18236 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights

File size: 4.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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;
23using HeuristicLab.Common;
24
25namespace HeuristicLab.Problems.DataAnalysis {
26  public class OnlineMeanAndVarianceCalculator : DeepCloneable {
27
28    private double m_oldM, m_newM, m_oldS, m_newS;
29    private int n;
30
31    private OnlineCalculatorError varianceErrorState;
32    public OnlineCalculatorError 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 OnlineCalculatorError errorState;
43    public OnlineCalculatorError 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 OnlineCalculatorError 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    protected OnlineMeanAndVarianceCalculator(OnlineMeanAndVarianceCalculator original, Cloner cloner = null)
70      : base(original, cloner) {
71      m_oldS = original.m_oldS;
72      m_oldM = original.m_oldM;
73      m_newS = original.m_newS;
74      m_newM = original.m_newM;
75      n = original.n;
76      errorState = original.errorState;
77      varianceErrorState = original.varianceErrorState;
78    }
79    public override IDeepCloneable Clone(Cloner cloner) {
80      return new OnlineMeanAndVarianceCalculator(this, cloner);
81    }
82
83    public void Reset() {
84      n = 0;
85      errorState = OnlineCalculatorError.InsufficientElementsAdded;
86      varianceErrorState = OnlineCalculatorError.InsufficientElementsAdded;
87    }
88
89    public void Add(double x) {
90      if (double.IsNaN(x) || double.IsInfinity(x) || x > 1E13 || x < -1E13 || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
91        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
92        varianceErrorState = varianceErrorState | OnlineCalculatorError.InvalidValueAdded;
93      } else {
94        n++;
95        // See Knuth TAOCP vol 2, 3rd edition, page 232
96        if (n == 1) {
97          m_oldM = m_newM = x;
98          m_oldS = 0.0;
99          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
100        } else {
101
102          varianceErrorState = varianceErrorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 2
103          m_newM = m_oldM + (x - m_oldM) / n;
104          m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
105
106          // set up for next iteration
107          m_oldM = m_newM;
108          m_oldS = m_newS;
109        }
110      }
111    }
112
113    public static void Calculate(IEnumerable<double> x, out double mean, out double variance, out OnlineCalculatorError meanErrorState, out OnlineCalculatorError varianceErrorState) {
114      OnlineMeanAndVarianceCalculator meanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
115      foreach (double xi in x) {
116        meanAndVarianceCalculator.Add(xi);
117      }
118      mean = meanAndVarianceCalculator.Mean;
119      variance = meanAndVarianceCalculator.Variance;
120      meanErrorState = meanAndVarianceCalculator.MeanErrorState;
121      varianceErrorState = meanAndVarianceCalculator.VarianceErrorState;
122    }
123  }
124}
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