[4951] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 |
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| 24 | namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
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| 25 | public class OnlineMeanAndVarianceCalculator {
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| 26 |
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| 27 | private double m_oldM, m_newM, m_oldS, m_newS;
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| 28 | private int n;
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| 29 |
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| 30 | public double Variance {
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| 31 | get {
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| 32 | return (n > 1) ? m_newS / (n - 1) : 0.0;
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| 33 | }
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| 34 | }
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| 35 |
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| 36 | public double PopulationVariance {
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| 37 | get {
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| 38 | return (n > 0) ? m_newS / n : 0.0;
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| 39 | }
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| 40 | }
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| 41 |
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| 42 | public double Mean {
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| 43 | get {
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| 44 | return (n > 0) ? m_newM : 0.0;
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| 45 | }
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| 46 | }
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| 47 |
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| 48 | public int Count {
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| 49 | get { return n; }
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| 50 | }
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| 51 |
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| 52 | public OnlineMeanAndVarianceCalculator() {
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| 53 | Reset();
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| 54 | }
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| 55 |
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| 56 | public void Reset() {
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| 57 | n = 0;
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| 58 | }
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| 59 |
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| 60 | public void Add(double x) {
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| 61 | if (double.IsNaN(x) || double.IsInfinity(x)) {
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| 62 | throw new ArgumentException("Mean and variance are not defined for NaN or infinity elements");
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| 63 | } else {
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| 64 | n++;
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| 65 | // See Knuth TAOCP vol 2, 3rd edition, page 232
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| 66 | if (n == 1) {
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| 67 | m_oldM = m_newM = x;
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| 68 | m_oldS = 0.0;
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| 69 | } else {
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| 70 | m_newM = m_oldM + (x - m_oldM) / n;
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| 71 | m_newS = m_oldS + (x - m_oldM) * (x - m_newM);
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| 72 |
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| 73 | // set up for next iteration
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| 74 | m_oldM = m_newM;
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| 75 | m_oldS = m_newS;
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| 76 | }
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| 77 | }
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| 78 | }
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| 79 | }
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| 80 | }
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