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annotate src/org/eclipse/jetty/util/statistic/SampleStatistic.java @ 802:3428c60d7cfc
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author | Franklin Schmidt <fschmidt@gmail.com> |
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date | Wed, 07 Sep 2016 21:15:48 -0600 |
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1 // |
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2 // ======================================================================== |
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3 // Copyright (c) 1995-2014 Mort Bay Consulting Pty. Ltd. |
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4 // ------------------------------------------------------------------------ |
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5 // All rights reserved. This program and the accompanying materials |
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6 // are made available under the terms of the Eclipse Public License v1.0 |
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7 // and Apache License v2.0 which accompanies this distribution. |
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8 // |
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9 // The Eclipse Public License is available at |
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10 // http://www.eclipse.org/legal/epl-v10.html |
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11 // |
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12 // The Apache License v2.0 is available at |
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13 // http://www.opensource.org/licenses/apache2.0.php |
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14 // |
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15 // You may elect to redistribute this code under either of these licenses. |
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16 // ======================================================================== |
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17 // |
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18 |
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19 package org.eclipse.jetty.util.statistic; |
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20 |
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21 import java.util.concurrent.atomic.AtomicLong; |
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22 |
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23 import org.eclipse.jetty.util.Atomics; |
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24 |
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25 |
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26 /* ------------------------------------------------------------ */ |
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27 /** |
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28 * SampledStatistics |
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29 * <p> |
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30 * Provides max, total, mean, count, variance, and standard |
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31 * deviation of continuous sequence of samples. |
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32 * <p> |
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33 * Calculates estimates of mean, variance, and standard deviation |
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34 * characteristics of a sample using a non synchronized |
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35 * approximation of the on-line algorithm presented |
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36 * in Donald Knuth's Art of Computer Programming, Volume 2, |
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37 * Seminumerical Algorithms, 3rd edition, page 232, |
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38 * Boston: Addison-Wesley. that cites a 1962 paper by B.P. Welford |
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39 * that can be found by following the link http://www.jstor.org/pss/1266577 |
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40 * <p> |
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41 * This algorithm is also described in Wikipedia at |
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42 * http://en.wikipedia.org/w/index.php?title=Algorithms_for_calculating_variance§ion=4#On-line_algorithm |
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43 */ |
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44 public class SampleStatistic |
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45 { |
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46 protected final AtomicLong _max = new AtomicLong(); |
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47 protected final AtomicLong _total = new AtomicLong(); |
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48 protected final AtomicLong _count = new AtomicLong(); |
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49 protected final AtomicLong _totalVariance100 = new AtomicLong(); |
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50 |
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51 public void reset() |
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52 { |
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53 _max.set(0); |
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54 _total.set(0); |
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55 _count.set(0); |
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56 _totalVariance100.set(0); |
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57 } |
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58 |
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59 public void set(final long sample) |
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60 { |
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61 long total = _total.addAndGet(sample); |
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62 long count = _count.incrementAndGet(); |
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63 |
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64 if (count>1) |
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65 { |
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66 long mean10 = total*10/count; |
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67 long delta10 = sample*10 - mean10; |
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68 _totalVariance100.addAndGet(delta10*delta10); |
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69 } |
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70 |
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71 Atomics.updateMax(_max, sample); |
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72 } |
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73 |
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74 /** |
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75 * @return the max value |
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76 */ |
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77 public long getMax() |
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78 { |
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79 return _max.get(); |
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80 } |
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81 |
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82 public long getTotal() |
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83 { |
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84 return _total.get(); |
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85 } |
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86 |
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87 public long getCount() |
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88 { |
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89 return _count.get(); |
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90 } |
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91 |
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92 public double getMean() |
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93 { |
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94 return (double)_total.get()/_count.get(); |
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95 } |
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96 |
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97 public double getVariance() |
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98 { |
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99 final long variance100 = _totalVariance100.get(); |
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100 final long count = _count.get(); |
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101 |
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102 return count>1?((double)variance100)/100.0/(count-1):0.0; |
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103 } |
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104 |
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105 public double getStdDev() |
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106 { |
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107 return Math.sqrt(getVariance()); |
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108 } |
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109 } |