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Posted to commits@datasketches.apache.org by al...@apache.org on 2019/05/29 22:12:28 UTC
[incubator-datasketches-cpp] branch theta updated: error bounds
This is an automated email from the ASF dual-hosted git repository.
alsay pushed a commit to branch theta
in repository https://gitbox.apache.org/repos/asf/incubator-datasketches-cpp.git
The following commit(s) were added to refs/heads/theta by this push:
new e6f994f error bounds
e6f994f is described below
commit e6f994ff289c869cd26762a5b86fc9e205c65784
Author: AlexanderSaydakov <Al...@users.noreply.github.com>
AuthorDate: Wed May 29 15:12:09 2019 -0700
error bounds
---
theta/include/binomial_bounds.hpp | 444 ++++++++++++++++++++++++++++++++++++
theta/include/theta_sketch.hpp | 4 +-
theta/include/theta_sketch_impl.hpp | 11 +-
theta/test/theta_sketch_test.cpp | 62 +++++
4 files changed, 515 insertions(+), 6 deletions(-)
diff --git a/theta/include/binomial_bounds.hpp b/theta/include/binomial_bounds.hpp
new file mode 100644
index 0000000..dc0d0ad
--- /dev/null
+++ b/theta/include/binomial_bounds.hpp
@@ -0,0 +1,444 @@
+/*
+ * Copyright 2019, Verizon Media.
+ * Licensed under the terms of the Apache License 2.0. See LICENSE file at the project root for terms.
+ */
+
+#ifndef BINOMIAL_BOUNDS_HPP_
+#define BINOMIAL_BOUNDS_HPP_
+
+#include <algorithm>
+#include <cmath>
+
+/*
+ * This class enables the estimation of error bounds given a sample set size, the sampling
+ * probability theta, the number of standard deviations and a simple noDataSeen flag. This can
+ * be used to estimate error bounds for fixed threshold sampling as well as the error bounds
+ * calculations for sketches.
+ *
+ * author Alexander Saydakov
+ * author Lee Rhodes
+ * author Kevin Lang
+ */
+
+namespace datasketches {
+
+class binomial_bounds {
+
+public:
+ static double get_lower_bound(unsigned long long num_samples, double theta, unsigned num_std_devs) {
+ check_theta(theta);
+ check_num_std_devs(num_std_devs);
+ const double estimate = num_samples / theta;
+ const double lb = compute_approx_binomial_lower_bound(num_samples, theta, num_std_devs);
+ return std::min(estimate, std::max(static_cast<double>(num_samples), lb));
+ }
+
+ static double get_upper_bound(unsigned long long num_samples, double theta, unsigned num_std_devs) {
+ check_theta(theta);
+ check_num_std_devs(num_std_devs);
+ const double estimate = num_samples / theta;
+ const double ub = compute_approx_binomial_upper_bound(num_samples, theta, num_std_devs);
+ return std::max(estimate, ub);
+ }
+
+private:
+ static constexpr double delta_of_num_std_devs[] = {
+ 0.5000000000000000000, // not actually using this value
+ 0.1586553191586026479,
+ 0.0227502618904135701,
+ 0.0013498126861731796
+ };
+
+ static constexpr double lb_equiv_table[] = {
+ 1.0, 2.0, 3.0, // fake values for k = 0
+ 0.78733703534118149, 3.14426768537558132, 13.56789685109913535, // k = 1
+ 0.94091379266077979, 2.64699271711145911, 6.29302733018320737, // k = 2
+ 0.96869128474958188, 2.46531676590527127, 4.97375283467403051, // k = 3
+ 0.97933572521046131, 2.37418810664669877, 4.44899975481712318, // k = 4
+ 0.98479165917274258, 2.31863116255024693, 4.16712379778553554, // k = 5
+ 0.98806033915698777, 2.28075536565225434, 3.99010556144099837, // k = 6
+ 0.99021896790580399, 2.25302005857281529, 3.86784477136922078, // k = 7
+ 0.99174267079089873, 2.23168103978522936, 3.77784896945266269, // k = 8
+ 0.99287147837287648, 2.21465899260871879, 3.70851932988722410, // k = 9
+ 0.99373900046805375, 2.20070155496262032, 3.65326029076638292, // k = 10
+ 0.99442519013851438, 2.18900651202670815, 3.60803817612955413, // k = 11
+ 0.99498066823221620, 2.17903457780744247, 3.57024330407946877, // k = 12
+ 0.99543899410224412, 2.17040883161922693, 3.53810982030634591, // k = 13
+ 0.99582322541263579, 2.16285726913676513, 3.51039837124298515, // k = 14
+ 0.99614973311747690, 2.15617827879603396, 3.48621230377099778, // k = 15
+ 0.99643042892560629, 2.15021897666090922, 3.46488605693562590, // k = 16
+ 0.99667418783778317, 2.14486114872480016, 3.44591466064832730, // k = 17
+ 0.99688774875812669, 2.14001181420209718, 3.42890765690452781, // k = 18
+ 0.99707632299691795, 2.13559675336844634, 3.41355809420343803, // k = 19
+ 0.99724399084971083, 2.13155592217421486, 3.39962113251016262, // k = 20
+ 0.99739400151915447, 2.12784018863251845, 3.38689892877548004, // k = 21
+ 0.99752896842633731, 2.12440890875851096, 3.37522975271599535, // k = 22
+ 0.99765101725122918, 2.12122815311133195, 3.36448003577621080, // k = 23
+ 0.99776189496810730, 2.11826934724291505, 3.35453840911279144, // k = 24
+ 0.99786304821586214, 2.11550823850916458, 3.34531123809287578, // k = 25
+ 0.99795568665180667, 2.11292409529477254, 3.33671916527694634, // k = 26
+ 0.99804083063483517, 2.11049908609763293, 3.32869446834217797, // k = 27
+ 0.99811933910984862, 2.10821776918189130, 3.32117898316676019, // k = 28
+ 0.99819195457286014, 2.10606671027090897, 3.31412243534683171, // k = 29
+ 0.99825930555178388, 2.10403415237001923, 3.30748113008135647, // k = 30
+ 0.99832193858154028, 2.10210975877822648, 3.30121691946897045, // k = 31
+ 0.99838032666573895, 2.10028440670842542, 3.29529629751144171, // k = 32
+ 0.99843488390555990, 2.09855000145353188, 3.28968974413223236, // k = 33
+ 0.99848596721417948, 2.09689934193824001, 3.28437111460505093, // k = 34
+ 0.99853390005924325, 2.09532599155502908, 3.27931717312372939, // k = 35
+ 0.99857895741078551, 2.09382418262592296, 3.27450718840060517, // k = 36
+ 0.99862138880970974, 2.09238872751677718, 3.26992261182860489, // k = 37
+ 0.99866141580770318, 2.09101494715108061, 3.26554677962434425, // k = 38
+ 0.99869923565267982, 2.08969860402822860, 3.26136468165239535, // k = 39
+ 0.99873502010169091, 2.08843585627218431, 3.25736275677081721, // k = 40
+ 0.99876893292508839, 2.08722321436752623, 3.25352872241415980, // k = 41
+ 0.99880111078502409, 2.08605749165553789, 3.24985141664350863, // k = 42
+ 0.99883168573342118, 2.08493577529222307, 3.24632068399498053, // k = 43
+ 0.99886077231613513, 2.08385540129560809, 3.24292724848112357, // k = 44
+ 0.99888847451828155, 2.08281392374021834, 3.23966263299664092, // k = 45
+ 0.99891488795844907, 2.08180908991394631, 3.23651906111521726, // k = 46
+ 0.99894010085196783, 2.08083882998420222, 3.23348939240611344, // k = 47
+ 0.99896419358239541, 2.07990122528650545, 3.23056705515594444, // k = 48
+ 0.99898723510594323, 2.07899450946285924, 3.22774598963252402, // k = 49
+ 0.99900929266780736, 2.07811704477046533, 3.22502059972006805, // k = 50
+ 0.99903043086155208, 2.07726730587160091, 3.22238570890294795, // k = 51
+ 0.99905070073845081, 2.07644388314946582, 3.21983651940365689, // k = 52
+ 0.99907015770423868, 2.07564546080757850, 3.21736857351049821, // k = 53
+ 0.99908884779227947, 2.07487081196367740, 3.21497773796417619, // k = 54
+ 0.99910681586905525, 2.07411879634256024, 3.21266015316183484, // k = 55
+ 0.99912410177549305, 2.07338834403498140, 3.21041222805715165, // k = 56
+ 0.99914074347179849, 2.07267845454973099, 3.20823061166797174, // k = 57
+ 0.99915677607464204, 2.07198819052374006, 3.20611216970604573, // k = 58
+ 0.99917223149395795, 2.07131667846186929, 3.20405396962596001, // k = 59
+ 0.99918714153457699, 2.07066309019154460, 3.20205326110445299, // k = 60
+ 0.99920153247185794, 2.07002665203046377, 3.20010746990493544, // k = 61
+ 0.99921543193525508, 2.06940663431663552, 3.19821417453343315, // k = 62
+ 0.99922886570365677, 2.06880235245998279, 3.19637109973109546, // k = 63
+ 0.99924185357357942, 2.06821315729285971, 3.19457610621114441, // k = 64
+ 0.99925441845175555, 2.06763843812092318, 3.19282717869864996, // k = 65
+ 0.99926658263325407, 2.06707761824370095, 3.19112241228646099, // k = 66
+ 0.99927836173816331, 2.06653015295219689, 3.18946001739936946, // k = 67
+ 0.99928977431994781, 2.06599552505539918, 3.18783829446098821, // k = 68
+ 0.99930083753795884, 2.06547324585920933, 3.18625564538041317, // k = 69
+ 0.99931156864562354, 2.06496285191821016, 3.18471055124089730, // k = 70
+ 0.99932197985521043, 2.06446390392778767, 3.18320157510865442, // k = 71
+ 0.99933208559809827, 2.06397598606787369, 3.18172735837393361, // k = 72
+ 0.99934190032416836, 2.06349869971447220, 3.18028661102792398, // k = 73
+ 0.99935143390791836, 2.06303166975550312, 3.17887810481605015, // k = 74
+ 0.99936070171270330, 2.06257453607466346, 3.17750067581857820, // k = 75
+ 0.99936971103502970, 2.06212696042919674, 3.17615321728274580, // k = 76
+ 0.99937847392385493, 2.06168861430600714, 3.17483467831510779, // k = 77
+ 0.99938700168914352, 2.06125918927764928, 3.17354405480557489, // k = 78
+ 0.99939530099953799, 2.06083838987589729, 3.17228039269048168, // k = 79
+ 0.99940338278830154, 2.06042593411496000, 3.17104278166036124, // k = 80
+ 0.99941125463777780, 2.06002155276328835, 3.16983035274597569, // k = 81
+ 0.99941892470027938, 2.05962498741951094, 3.16864227952240185, // k = 82
+ 0.99942640059737187, 2.05923599161263837, 3.16747776846497686, // k = 83
+ 0.99943368842187397, 2.05885433061945378, 3.16633606416374391, // k = 84
+ 0.99944079790603269, 2.05847977868873500, 3.16521644518826406, // k = 85
+ 0.99944773295734990, 2.05811212058944193, 3.16411821883858124, // k = 86
+ 0.99945450059186669, 2.05775114781260982, 3.16304072400711789, // k = 87
+ 0.99946110646314423, 2.05739666442039493, 3.16198332650733960, // k = 88
+ 0.99946755770463369, 2.05704847678819647, 3.16094541781455973, // k = 89
+ 0.99947385746861528, 2.05670640500335367, 3.15992641851471490, // k = 90
+ 0.99948001256305474, 2.05637027420314666, 3.15892576988736096, // k = 91
+ 0.99948602689656241, 2.05603991286400856, 3.15794293484717059, // k = 92
+ 0.99949190674294641, 2.05571516158917689, 3.15697740043813724, // k = 93
+ 0.99949765436329585, 2.05539586490317561, 3.15602867309343083, // k = 94
+ 0.99950327557880314, 2.05508187237845164, 3.15509627710042651, // k = 95
+ 0.99950877461972709, 2.05477304104951486, 3.15417975753007340, // k = 96
+ 0.99951415481862682, 2.05446923022574879, 3.15327867462917766, // k = 97
+ 0.99951942042375208, 2.05417030908833453, 3.15239260700215596, // k = 98
+ 0.99952457390890004, 2.05387614661762541, 3.15152114915238712, // k = 99
+ 0.99952962005008317, 2.05358662050909402, 3.15066390921020911, // k = 100
+ 0.99953456216121594, 2.05330161104427589, 3.14982051097524618, // k = 101
+ 0.99953940176368405, 2.05302100378725072, 3.14899059183684926, // k = 102
+ 0.99954414373920031, 2.05274468493067275, 3.14817379948561893, // k = 103
+ 0.99954879047621148, 2.05247255013657082, 3.14736979964868624, // k = 104
+ 0.99955334485656522, 2.05220449388099269, 3.14657826610371671, // k = 105
+ 0.99955780993869325, 2.05194041831310869, 3.14579888316276879, // k = 106
+ 0.99956218652590678, 2.05168022402710903, 3.14503134811607765, // k = 107
+ 0.99956647932785359, 2.05142381889103831, 3.14427536967733090, // k = 108
+ 0.99957069025060719, 2.05117111251445294, 3.14353066260227365, // k = 109
+ 0.99957482032178291, 2.05092201793428330, 3.14279695558593630, // k = 110
+ 0.99957887261450651, 2.05067645094720774, 3.14207398336887422, // k = 111
+ 0.99958284988383639, 2.05043432833224415, 3.14136149076028914, // k = 112
+ 0.99958675435604505, 2.05019557189746138, 3.14065923143530767, // k = 113
+ 0.99959058650074439, 2.04996010556124020, 3.13996696426707445, // k = 114
+ 0.99959434898201494, 2.04972785368377686, 3.13928445867830419, // k = 115
+ 0.99959804437042976, 2.04949874512311681, 3.13861149103462367, // k = 116
+ 0.99960167394553423, 2.04927271043337100, 3.13794784369528656, // k = 117
+ 0.99960523957651048, 2.04904968140490951, 3.13729330661277572, // k = 118
+ 0.99960874253329735, 2.04882959397491504, 3.13664767767019725, // k = 119
+ 0.99961218434327748, 2.04861238220240693, 3.13601075688413289 // k = 120
+ };
+
+ static constexpr double ub_equiv_table[] = {
+ 1.0, 2.0, 3.0, // fake values for k = 0
+ 0.99067760836669549, 1.75460517119302040, 2.48055626001627161, // k = 1
+ 0.99270518097577565, 1.78855957509907171, 2.53863835259832626, // k = 2
+ 0.99402032633599902, 1.81047286499563143, 2.57811676180597260, // k = 3
+ 0.99492607629539975, 1.82625928017762362, 2.60759550546498531, // k = 4
+ 0.99558653966013821, 1.83839160339161367, 2.63086812358551470, // k = 5
+ 0.99608981951632813, 1.84812399034444752, 2.64993712523727254, // k = 6
+ 0.99648648035983456, 1.85617372053235385, 2.66598485907860550, // k = 7
+ 0.99680750790483330, 1.86298655802610824, 2.67976541374471822, // k = 8
+ 0.99707292880049181, 1.86885682585270274, 2.69178781407745760, // k = 9
+ 0.99729614928489241, 1.87398826101983218, 2.70241106542158604, // k = 10
+ 0.99748667952445658, 1.87852708449801753, 2.71189717290596377, // k = 11
+ 0.99765127712748836, 1.88258159501103250, 2.72044290303773550, // k = 12
+ 0.99779498340305395, 1.88623391878036273, 2.72819957382063194, // k = 13
+ 0.99792160418357412, 1.88954778748873764, 2.73528576807902368, // k = 14
+ 0.99803398604944960, 1.89257337682371940, 2.74179612106766513, // k = 15
+ 0.99813449883217231, 1.89535099316557876, 2.74780718300419835, // k = 16
+ 0.99822494122659577, 1.89791339232732525, 2.75338173141955167, // k = 17
+ 0.99830679915913834, 1.90028752122407241, 2.75857186416826039, // k = 18
+ 0.99838117410831728, 1.90249575897183831, 2.76342117562634826, // k = 19
+ 0.99844913407071090, 1.90455689090418900, 2.76796659454200267, // k = 20
+ 0.99851147736424650, 1.90648682834171268, 2.77223944710058845, // k = 21
+ 0.99856879856019987, 1.90829917277082473, 2.77626682032629901, // k = 22
+ 0.99862183849734265, 1.91000561415842185, 2.78007199816156003, // k = 23
+ 0.99867096266018507, 1.91161621560812023, 2.78367524259661536, // k = 24
+ 0.99871656986212543, 1.91313978579765376, 2.78709435016625662, // k = 25
+ 0.99875907577771272, 1.91458400425526065, 2.79034488416175463, // k = 26
+ 0.99879885565047744, 1.91595563175945927, 2.79344064132371273, // k = 27
+ 0.99883610756373287, 1.91726064301425936, 2.79639384757751941, // k = 28
+ 0.99887095169674467, 1.91850441099725799, 2.79921543574803877, // k = 29
+ 0.99890379414739527, 1.91969155477030995, 2.80191513182441554, // k = 30
+ 0.99893466279047516, 1.92082633358913313, 2.80450167352080371, // k = 31
+ 0.99896392088177777, 1.92191254955568525, 2.80698295731653502, // k = 32
+ 0.99899147889385631, 1.92295362479495680, 2.80936614404217266, // k = 33
+ 0.99901764688726757, 1.92395267400968351, 2.81165765979318394, // k = 34
+ 0.99904238606342233, 1.92491244978191389, 2.81386337393604435, // k = 35
+ 0.99906590152386343, 1.92583552644848055, 2.81598868034527072, // k = 36
+ 0.99908829040739988, 1.92672418013918900, 2.81803841726804194, // k = 37
+ 0.99910959420023460, 1.92758051694144683, 2.82001709302821268, // k = 38
+ 0.99912996403594434, 1.92840654943159961, 2.82192875763732332, // k = 39
+ 0.99914930224576892, 1.92920397044028391, 2.82377730628954282, // k = 40
+ 0.99916781270195543, 1.92997447498220254, 2.82556612075063640, // k = 41
+ 0.99918553179077207, 1.93071949211818605, 2.82729843191989971, // k = 42
+ 0.99920250730914972, 1.93144048613876862, 2.82897728689417249, // k = 43
+ 0.99921873345181211, 1.93213870990595638, 2.83060537017752267, // k = 44
+ 0.99923435180002684, 1.93281536508689555, 2.83218527795750674, // k = 45
+ 0.99924930425362390, 1.93347145882316340, 2.83371938965598247, // k = 46
+ 0.99926370394567243, 1.93410820221384938, 2.83520990872793277, // k = 47
+ 0.99927750755296074, 1.93472643138986200, 2.83665891945119597, // k = 48
+ 0.99929082941537217, 1.93532697329771963, 2.83806833931606661, // k = 49
+ 0.99930366295501472, 1.93591074716263734, 2.83943997143404658, // k = 50
+ 0.99931598804721489, 1.93647857274021362, 2.84077557836653227, // k = 51
+ 0.99932789059798210, 1.93703110239354714, 2.84207662106302905, // k = 52
+ 0.99933946180485123, 1.93756904936378760, 2.84334468086129277, // k = 53
+ 0.99935053819703512, 1.93809302131219852, 2.84458116874117195, // k = 54
+ 0.99936126637970801, 1.93860365411038060, 2.84578731838604426, // k = 55
+ 0.99937166229284458, 1.93910149816429112, 2.84696443486512862, // k = 56
+ 0.99938169190727422, 1.93958709548454067, 2.84811369085281285, // k = 57
+ 0.99939136927613959, 1.94006085573701625, 2.84923617230361970, // k = 58
+ 0.99940074328745254, 1.94052339623206649, 2.85033291216254270, // k = 59
+ 0.99940993070470086, 1.94097508636855309, 2.85140492437699322, // k = 60
+ 0.99941868577388959, 1.94141633372043998, 2.85245314430358121, // k = 61
+ 0.99942734443487780, 1.94184757038001976, 2.85347839582286156, // k = 62
+ 0.99943556385736088, 1.94226915100517772, 2.85448160365493209, // k = 63
+ 0.99944374522542034, 1.94268143723749631, 2.85546346373061510, // k = 64
+ 0.99945159955424856, 1.94308482059116727, 2.85642486111805738, // k = 65
+ 0.99945915301904620, 1.94347956957849988, 2.85736639994965458, // k = 66
+ 0.99946660663832176, 1.94386600964031686, 2.85828887832701639, // k = 67
+ 0.99947383703224091, 1.94424436597356021, 2.85919278275500233, // k = 68
+ 0.99948075442870277, 1.94461502153473020, 2.86007887186090670, // k = 69
+ 0.99948766082269458, 1.94497821937304138, 2.86094774077355396, // k = 70
+ 0.99949422748713346, 1.94533411296001191, 2.86179981848076181, // k = 71
+ 0.99950070756119658, 1.94568300035135167, 2.86263579405672886, // k = 72
+ 0.99950704321753392, 1.94602523449961495, 2.86345610449197352, // k = 73
+ 0.99951320334216121, 1.94636083782822311, 2.86426125541271404, // k = 74
+ 0.99951920293474927, 1.94669011080745236, 2.86505169255406145, // k = 75
+ 0.99952501670378524, 1.94701327348536779, 2.86582788270862920, // k = 76
+ 0.99953071209267819, 1.94733044372333097, 2.86659027602854621, // k = 77
+ 0.99953632734991515, 1.94764180764266825, 2.86733927778843167, // k = 78
+ 0.99954171164873173, 1.94794766430732125, 2.86807526143834934, // k = 79
+ 0.99954699274462655, 1.94824807472994621, 2.86879864789403882, // k = 80
+ 0.99955216611081710, 1.94854317889829076, 2.86950970901679625, // k = 81
+ 0.99955730019613043, 1.94883320227168610, 2.87020887436986527, // k = 82
+ 0.99956213770650493, 1.94911826561721568, 2.87089648477021342, // k = 83
+ 0.99956704264963037, 1.94939848545763539, 2.87157281693902178, // k = 84
+ 0.99957166306481327, 1.94967401618316671, 2.87223821840905202, // k = 85
+ 0.99957632713136491, 1.94994497791333288, 2.87289293193450135, // k = 86
+ 0.99958087233392234, 1.95021155752212394, 2.87353731228213860, // k = 87
+ 0.99958532555996271, 1.95047376805584349, 2.87417154907075201, // k = 88
+ 0.99958956246481989, 1.95073180380688882, 2.87479599765507032, // k = 89
+ 0.99959389351869277, 1.95098572880579013, 2.87541081987382086, // k = 90
+ 0.99959807862052230, 1.95123574036898617, 2.87601637401948551, // k = 91
+ 0.99960214057801977, 1.95148186921983324, 2.87661283691068093, // k = 92
+ 0.99960607527256684, 1.95172415829728152, 2.87720042968334155, // k = 93
+ 0.99960996433179616, 1.95196280898670693, 2.87777936649376898, // k = 94
+ 0.99961379137860717, 1.95219787713926962, 2.87834989933620022, // k = 95
+ 0.99961756088146103, 1.95242944583677058, 2.87891216133900230, // k = 96
+ 0.99962125605327401, 1.95265762420910960, 2.87946647367488140, // k = 97
+ 0.99962486179100551, 1.95288245314810638, 2.88001290210658567, // k = 98
+ 0.99962843240297161, 1.95310404286672679, 2.88055166523392359, // k = 99
+ 0.99963187276145504, 1.95332251980147475, 2.88108300006589957, // k = 100
+ 0.99963525453173929, 1.95353785898848287, 2.88160703591438505, // k = 101
+ 0.99963855412988778, 1.95375019354571577, 2.88212393551896184, // k = 102
+ 0.99964190254169694, 1.95395953472205974, 2.88263389761985422, // k = 103
+ 0.99964506565942202, 1.95416607430155409, 2.88313700661564098, // k = 104
+ 0.99964834424233118, 1.95436972855640079, 2.88363350163803034, // k = 105
+ 0.99965136548857458, 1.95457068540693513, 2.88412349413960101, // k = 106
+ 0.99965436594726498, 1.95476896383092935, 2.88460710620208260, // k = 107
+ 0.99965736463468602, 1.95496457504532373, 2.88508450078833789, // k = 108
+ 0.99966034130443404, 1.95515761150707590, 2.88555580586194083, // k = 109
+ 0.99966326130828520, 1.95534810382198998, 2.88602118761679094, // k = 110
+ 0.99966601446035952, 1.95553622237747504, 2.88648066384146773, // k = 111
+ 0.99966887679593697, 1.95572186728168163, 2.88693444915907094, // k = 112
+ 0.99967161286551232, 1.95590523410490391, 2.88738271495714116, // k = 113
+ 0.99967435412270333, 1.95608626483223702, 2.88782540459769166, // k = 114
+ 0.99967701261934394, 1.95626497627117146, 2.88826277189363623, // k = 115
+ 0.99967963265157778, 1.95644153684824573, 2.88869486674335008, // k = 116
+ 0.99968216317182623, 1.95661589936000269, 2.88912184353694101, // k = 117
+ 0.99968479674396349, 1.95678821614791332, 2.88954376359643561, // k = 118
+ 0.99968729031337489, 1.95695842061650183, 2.88996069422501023, // k = 119
+ 0.99968963358631413, 1.95712651709766305, 2.89037285320668502 // k = 120
+ };
+
+ // our "classic" bounds, but now with continuity correction
+ static double cont_classic_lb(unsigned long long num_samples, double theta, double num_std_devs) {
+ const double n_hat = (num_samples - 0.5) / theta;
+ const double b = num_std_devs * std::sqrt((1.0 - theta) / theta);
+ const double d = 0.5 * b * std::sqrt((b * b) + (4.0 * n_hat));
+ const double center = n_hat + (0.5 * (b * b));
+ return (center - d);
+ }
+
+ // our "classic" bounds, but now with continuity correction
+ static double cont_classic_ub(unsigned long long num_samples, double theta, double num_std_devs) {
+ const double n_hat = (num_samples + 0.5) / theta;
+ const double b = num_std_devs * std::sqrt((1.0 - theta) / theta);
+ const double d = 0.5 * b * std::sqrt((b * b) + (4.0 * n_hat));
+ const double center = n_hat + (0.5 * (b * b));
+ return (center + d);
+ }
+
+ // This is a special purpose calculator for NStar, using a computational
+ // strategy inspired by its Bayesian definition. It is only appropriate
+ // for a very limited set of inputs. However, the procedure compute_approx_binomial_lower_bound()
+ // below does in fact only call it for suitably limited inputs.
+ // Outside of this limited range, two different bad things will happen.
+ // First, because we are not using logarithms, the values of intermediate
+ // quantities will exceed the dynamic range of doubles. Second, even if that
+ // problem were fixed, the running time of this procedure is essentially linear
+ // in est = (numSamples / p), and that can be Very, Very Big.
+ static unsigned long long special_n_star(unsigned long long num_samples, double p, double delta) {
+ const double q = 1.0 - p;
+ // Use a different algorithm if the following is true; this one will be too slow, or worse.
+ if ((num_samples / p) >= 500.0) throw std::invalid_argument("out of range");
+ double cur_term = std::pow(p, num_samples); // curTerm = posteriorProbability (k, k, p)
+ if (cur_term <= 1e-100) throw std::logic_error("out of range"); // sanity check for non-use of logarithms
+ double tot = cur_term;
+ unsigned long long m = num_samples;
+ while (tot <= delta) { // this test can fail even the first time
+ cur_term = (cur_term * q * (m)) / ((m + 1) - num_samples);
+ tot += cur_term;
+ m += 1;
+ }
+ // we have reached a state where tot > delta, so back up one
+ return (m - 1);
+ }
+
+ // The following procedure has very limited applicability.
+ // The above remarks about special_n_star() also apply here.
+ static unsigned long long special_n_prime_b(unsigned long long num_samples, double p, double delta) {
+ const double q = 1.0 - p;
+ const double one_minus_delta = 1.0 - delta;
+ double cur_term = std::pow(p, num_samples); // curTerm = posteriorProbability (k, k, p)
+ if (cur_term <= 1e-100) throw std::logic_error("out of range"); // sanity check for non-use of logarithms
+ double tot = cur_term;
+ unsigned long long m = num_samples;
+ while (tot < one_minus_delta) {
+ cur_term = (cur_term * q * (m)) / ((m + 1) - num_samples);
+ tot += cur_term;
+ m += 1;
+ }
+ return m; // no need to back up
+ }
+
+ static unsigned long long special_n_prime_f(unsigned long long num_samples, double p, double delta) {
+ // Use a different algorithm if the following is true; this one will be too slow, or worse.
+ if ((num_samples / p) >= 500.0) throw std::invalid_argument("out of range"); //A super-small delta could also make it slow.
+ return special_n_prime_b(num_samples + 1, p, delta);
+ }
+
+ // The following computes an approximation to the lower bound of a Frequentist
+ // confidence interval based on the tails of the Binomial distribution.
+ static double compute_approx_binomial_lower_bound(unsigned long long num_samples, double theta, unsigned num_std_devs) {
+ if (theta == 1) return num_samples;
+ if (num_samples == 0) return 0;
+ if (num_samples == 1) {
+ const double delta = delta_of_num_std_devs[num_std_devs];
+ const double raw_lb = std::log(1 - delta) / std::log(1 - theta);
+ return std::floor(raw_lb); // round down
+ }
+ if (num_samples > 120) {
+ // plenty of samples, so gaussian approximation to binomial distribution isn't too bad
+ const double raw_lb = cont_classic_lb(num_samples, theta, num_std_devs);
+ return (raw_lb - 0.5); // fake round down
+ }
+ // at this point we know 2 <= num_samples <= 120
+ if (theta > (1 - 1e-5)) { // empirically-determined threshold
+ return num_samples;
+ }
+ if (theta < (num_samples / 360.0)) { // empirically-determined threshold
+ // here we use the Gaussian approximation, but with a modified num_std_devs
+ const unsigned index = 3 * num_samples + (num_std_devs - 1);
+ const double raw_lb = cont_classic_lb(num_samples, theta, lb_equiv_table[index]);
+ return raw_lb - 0.5; // fake round down
+ }
+ // This is the most difficult range to approximate; we will compute an "exact" LB.
+ // We know that est <= 360, so specialNStar() shouldn't be ridiculously slow.
+ const double delta = delta_of_num_std_devs[num_std_devs];
+ return special_n_star(num_samples, theta, delta); // no need to round
+ }
+
+ // The following computes an approximation to the upper bound of a Frequentist
+ // confidence interval based on the tails of the Binomial distribution.
+ static double compute_approx_binomial_upper_bound(unsigned long long num_samples, double theta, unsigned num_std_devs) {
+ if (theta == 1) return num_samples;
+ if (num_samples == 0) {
+ const double delta = delta_of_num_std_devs[num_std_devs];
+ const double raw_ub = std::log(delta) / std::log(1 - theta);
+ return std::ceil(raw_ub); // round up
+ }
+ if (num_samples > 120) {
+ // plenty of samples, so gaussian approximation to binomial distribution isn't too bad
+ const double raw_ub = cont_classic_ub(num_samples, theta, num_std_devs);
+ return (raw_ub + 0.5); // fake round up
+ }
+ // at this point we know 2 <= num_samples <= 120
+ if (theta > (1 - 1e-5)) { // empirically-determined threshold
+ return num_samples + 1;
+ }
+ if (theta < (num_samples / 360.0)) { // empirically-determined threshold
+ // here we use the Gaussian approximation, but with a modified num_std_devs
+ const unsigned index = 3 * num_samples + (num_std_devs - 1);
+ const double raw_ub = cont_classic_ub(num_samples, theta, ub_equiv_table[index]);
+ return raw_ub + 0.5; // fake round up
+ }
+ // This is the most difficult range to approximate; we will compute an "exact" UB.
+ // We know that est <= 360, so specialNPrimeF() shouldn't be ridiculously slow.
+ const double delta = delta_of_num_std_devs[num_std_devs];
+ return special_n_prime_f(num_samples, theta, delta); // no need to round
+ }
+
+ static void check_theta(double theta) {
+ if (theta < 0 or theta > 1) {
+ throw std::invalid_argument("theta must be in [0, 1]");
+ }
+ }
+
+ static void check_num_std_devs(unsigned num_std_devs) {
+ if (num_std_devs < 1 or num_std_devs > 3) {
+ throw std::invalid_argument("num_std_devs must be 1, 2 or 3");
+ }
+ }
+
+};
+
+} /* namespace datasketches */
+
+# endif
diff --git a/theta/include/theta_sketch.hpp b/theta/include/theta_sketch.hpp
index 5ec618d..3eab496 100644
--- a/theta/include/theta_sketch.hpp
+++ b/theta/include/theta_sketch.hpp
@@ -45,8 +45,8 @@ public:
bool is_empty() const;
double get_estimate() const;
- double get_lower_bound(uint8_t num_std_dev) const;
- double get_upper_bound(uint8_t num_std_dev) const;
+ double get_lower_bound(uint8_t num_std_devs) const;
+ double get_upper_bound(uint8_t num_std_devs) const;
bool is_estimation_mode() const;
double get_theta() const;
uint64_t get_theta64() const;
diff --git a/theta/include/theta_sketch_impl.hpp b/theta/include/theta_sketch_impl.hpp
index 234585d..b33ae5a 100644
--- a/theta/include/theta_sketch_impl.hpp
+++ b/theta/include/theta_sketch_impl.hpp
@@ -13,6 +13,7 @@
#include "MurmurHash3.h"
#include "serde.hpp"
+#include "binomial_bounds.hpp"
namespace datasketches {
@@ -66,13 +67,15 @@ double theta_sketch_alloc<A>::get_estimate() const {
}
template<typename A>
-double theta_sketch_alloc<A>::get_lower_bound(uint8_t num_std_dev) const {
- return 0;
+double theta_sketch_alloc<A>::get_lower_bound(uint8_t num_std_devs) const {
+ if (!is_estimation_mode()) return get_num_retained();
+ return binomial_bounds::get_lower_bound(get_num_retained(), get_theta(), num_std_devs);
}
template<typename A>
-double theta_sketch_alloc<A>::get_upper_bound(uint8_t num_std_dev) const {
- return 0;
+double theta_sketch_alloc<A>::get_upper_bound(uint8_t num_std_devs) const {
+ if (!is_estimation_mode()) return get_num_retained();
+ return binomial_bounds::get_upper_bound(get_num_retained(), get_theta(), num_std_devs);
}
template<typename A>
diff --git a/theta/test/theta_sketch_test.cpp b/theta/test/theta_sketch_test.cpp
index 42e05af..62f6192 100644
--- a/theta/test/theta_sketch_test.cpp
+++ b/theta/test/theta_sketch_test.cpp
@@ -37,27 +37,36 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT(!update_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, update_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, update_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, update_sketch.get_upper_bound(1));
compact_theta_sketch compact_sketch = update_sketch.compact();
CPPUNIT_ASSERT(compact_sketch.is_empty());
CPPUNIT_ASSERT(!compact_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, compact_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, compact_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, compact_sketch.get_upper_bound(1));
}
void non_empty_no_retained_keys() {
update_theta_sketch update_sketch = update_theta_sketch::builder().set_p(0.001).build();
update_sketch.update(1);
+ //update_sketch.to_stream(std::cerr);
CPPUNIT_ASSERT_EQUAL(0U, update_sketch.get_num_retained());
CPPUNIT_ASSERT(!update_sketch.is_empty());
CPPUNIT_ASSERT(update_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(0.0, update_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, update_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT(update_sketch.get_upper_bound(1) > 0);
compact_theta_sketch compact_sketch = update_sketch.compact();
CPPUNIT_ASSERT_EQUAL(0U, compact_sketch.get_num_retained());
CPPUNIT_ASSERT(!compact_sketch.is_empty());
CPPUNIT_ASSERT(compact_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(0.0, compact_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, compact_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT(compact_sketch.get_upper_bound(1) > 0);
}
void single_item() {
@@ -67,12 +76,16 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT(!update_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_upper_bound(1));
compact_theta_sketch compact_sketch = update_sketch.compact();
CPPUNIT_ASSERT(!compact_sketch.is_empty());
CPPUNIT_ASSERT(!compact_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_upper_bound(1));
}
void resize_exact() {
@@ -82,28 +95,37 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT(!update_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, update_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(2000.0, update_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(2000.0, update_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(2000.0, update_sketch.get_upper_bound(1));
compact_theta_sketch compact_sketch = update_sketch.compact();
CPPUNIT_ASSERT(!compact_sketch.is_empty());
CPPUNIT_ASSERT(!compact_sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(1.0, compact_sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(2000.0, compact_sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(2000.0, compact_sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(2000.0, compact_sketch.get_upper_bound(1));
}
void estimation() {
update_theta_sketch update_sketch = update_theta_sketch::builder().set_resize_factor(update_theta_sketch::resize_factor::X1).build();
const int n = 8000;
for (int i = 0; i < n; i++) update_sketch.update(i);
+ //update_sketch.to_stream(std::cerr);
CPPUNIT_ASSERT(!update_sketch.is_empty());
CPPUNIT_ASSERT(update_sketch.is_estimation_mode());
CPPUNIT_ASSERT(update_sketch.get_theta() < 1.0);
CPPUNIT_ASSERT_DOUBLES_EQUAL((double) n, update_sketch.get_estimate(), n * 0.01);
+ CPPUNIT_ASSERT(update_sketch.get_lower_bound(1) < n);
+ CPPUNIT_ASSERT(update_sketch.get_upper_bound(1) > n);
compact_theta_sketch compact_sketch = update_sketch.compact();
CPPUNIT_ASSERT(!compact_sketch.is_empty());
CPPUNIT_ASSERT(compact_sketch.is_estimation_mode());
CPPUNIT_ASSERT(compact_sketch.get_theta() < 1.0);
CPPUNIT_ASSERT_DOUBLES_EQUAL((double) n, compact_sketch.get_estimate(), n * 0.01);
+ CPPUNIT_ASSERT(compact_sketch.get_lower_bound(1) < n);
+ CPPUNIT_ASSERT(compact_sketch.get_upper_bound(1) > n);
}
void deserialize_update_empty_from_java_as_base() {
@@ -116,6 +138,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(0U, sketchptr->get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_upper_bound(1));
}
void deserialize_update_empty_from_java_as_subclass() {
@@ -128,6 +152,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(0U, sketch.get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_upper_bound(1));
}
void deserialize_update_estimation_from_java_as_base() {
@@ -139,6 +165,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT(sketchptr->is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(5324U, sketchptr->get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(10000.0, sketchptr->get_estimate(), 10000 * 0.01);
+ CPPUNIT_ASSERT(sketchptr->get_lower_bound(1) < 10000);
+ CPPUNIT_ASSERT(sketchptr->get_upper_bound(1) > 10000);
}
void deserialize_update_estimation_from_java_as_subclass() {
@@ -150,6 +178,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT(sketch.is_estimation_mode());
CPPUNIT_ASSERT_EQUAL(5324U, sketch.get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(10000.0, sketch.get_estimate(), 10000 * 0.01);
+ CPPUNIT_ASSERT(sketch.get_lower_bound(1) < 10000);
+ CPPUNIT_ASSERT(sketch.get_upper_bound(1) > 10000);
}
void deserialize_compact_empty_from_java_as_base() {
@@ -162,6 +192,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(0U, sketchptr->get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, sketchptr->get_upper_bound(1));
}
void deserialize_compact_empty_from_java_as_subclass() {
@@ -174,6 +206,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(0U, sketch.get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(0.0, sketch.get_upper_bound(1));
}
void deserialize_single_item_from_java_as_base() {
@@ -186,6 +220,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(1U, sketchptr->get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_theta());
CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_estimate());
+ CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(1.0, sketchptr->get_upper_bound(1));
}
void deserialize_single_item_from_java_as_subclass() {
@@ -198,6 +234,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(1U, sketch.get_num_retained());
CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_theta());
CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(1.0, sketch.get_upper_bound(1));
}
void deserialize_compact_estimation_from_java_as_base() {
@@ -211,6 +249,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(4342U, sketchptr->get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.531700444213199, sketchptr->get_theta(), 1e-10);
CPPUNIT_ASSERT_DOUBLES_EQUAL(8166.25234614053, sketchptr->get_estimate(), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(7996.956955317471, sketchptr->get_lower_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(8339.090301078124, sketchptr->get_upper_bound(2), 1e-10);
// the same construction process in Java must have produced exactly the same sketch
update_theta_sketch update_sketch = update_theta_sketch::builder().build();
@@ -219,6 +259,12 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(update_sketch.get_num_retained(), sketchptr->get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_theta(), sketchptr->get_theta(), 1e-10);
CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_estimate(), sketchptr->get_estimate(), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(1), sketchptr->get_lower_bound(1), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(1), sketchptr->get_upper_bound(1), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(2), sketchptr->get_lower_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(2), sketchptr->get_upper_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(3), sketchptr->get_lower_bound(3), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(3), sketchptr->get_upper_bound(3), 1e-10);
compact_theta_sketch compact_sketch = update_sketch.compact();
// the sketches are ordered, so the iteration sequence must match exactly
auto iter = sketchptr->begin();
@@ -238,6 +284,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(4342U, sketch.get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(0.531700444213199, sketch.get_theta(), 1e-10);
CPPUNIT_ASSERT_DOUBLES_EQUAL(8166.25234614053, sketch.get_estimate(), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(7996.956955317471, sketch.get_lower_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(8339.090301078124, sketch.get_upper_bound(2), 1e-10);
update_theta_sketch update_sketch = update_theta_sketch::builder().build();
const int n = 8192;
@@ -245,6 +293,12 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(update_sketch.get_num_retained(), sketch.get_num_retained());
CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_theta(), sketch.get_theta(), 1e-10);
CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_estimate(), sketch.get_estimate(), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(1), sketch.get_lower_bound(1), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(1), sketch.get_upper_bound(1), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(2), sketch.get_lower_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(2), sketch.get_upper_bound(2), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_lower_bound(3), sketch.get_lower_bound(3), 1e-10);
+ CPPUNIT_ASSERT_DOUBLES_EQUAL(update_sketch.get_upper_bound(3), sketch.get_upper_bound(3), 1e-10);
}
void serialize_deserialize_stream_and_bytes_equivalency() {
@@ -273,6 +327,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_num_retained(), deserialized_sketch_ptr2->get_num_retained());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_theta(), deserialized_sketch_ptr2->get_theta());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_estimate(), deserialized_sketch_ptr2->get_estimate());
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_lower_bound(1), deserialized_sketch_ptr2->get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_upper_bound(1), deserialized_sketch_ptr2->get_upper_bound(1));
// hash tables must be identical since they are restored from dumps, and iteration is deterministic
auto iter = deserialized_sketch_ptr1->begin();
for (auto key: *deserialized_sketch_ptr2) {
@@ -292,6 +348,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_num_retained(), deserialized_sketch2.get_num_retained());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_theta(), deserialized_sketch2.get_theta());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_estimate(), deserialized_sketch2.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_lower_bound(1), deserialized_sketch2.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_upper_bound(1), deserialized_sketch2.get_upper_bound(1));
// hash tables must be identical since they are restored from dumps, and iteration is deterministic
auto iter = deserialized_sketch1.begin();
for (auto key: deserialized_sketch2) {
@@ -322,6 +380,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_num_retained(), deserialized_sketch_ptr2->get_num_retained());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_theta(), deserialized_sketch_ptr2->get_theta());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_estimate(), deserialized_sketch_ptr2->get_estimate());
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_lower_bound(1), deserialized_sketch_ptr2->get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch_ptr1->get_upper_bound(1), deserialized_sketch_ptr2->get_upper_bound(1));
// the sketches are ordered, so the iteration sequence must match exactly
auto iter = deserialized_sketch_ptr1->begin();
for (auto key: *deserialized_sketch_ptr2) {
@@ -341,6 +401,8 @@ class theta_sketch_test: public CppUnit::TestFixture {
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_num_retained(), deserialized_sketch2.get_num_retained());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_theta(), deserialized_sketch2.get_theta());
CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_estimate(), deserialized_sketch2.get_estimate());
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_lower_bound(1), deserialized_sketch2.get_lower_bound(1));
+ CPPUNIT_ASSERT_EQUAL(deserialized_sketch1.get_upper_bound(1), deserialized_sketch2.get_upper_bound(1));
// the sketches are ordered, so the iteration sequence must match exactly
auto iter = deserialized_sketch1.begin();
for (auto key: deserialized_sketch2) {
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