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annotate src/org/eclipse/jetty/util/statistic/SampleStatistic.java @ 804:9f5ba30722bb

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remove org.eclipse.jetty.util.ajax
author Franklin Schmidt <fschmidt@gmail.com>
date Wed, 07 Sep 2016 21:28:02 -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&section=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 }