Last updated on 2026-02-12 03:52:11 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.0 | OK | ||||
| r-devel-linux-x86_64-debian-gcc | 1.5.0 | 14.72 | 195.47 | 210.19 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.5.0 | 54.00 | 470.94 | 524.94 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.0 | 49.00 | 438.00 | 487.00 | OK | |
| r-devel-macos-arm64 | 1.5.0 | 6.00 | 57.00 | 63.00 | ERROR | |
| r-devel-windows-x86_64 | 1.5.0 | 28.00 | 256.00 | 284.00 | OK | |
| r-patched-linux-x86_64 | 1.5.0 | 27.48 | 263.51 | 290.99 | OK | |
| r-release-linux-x86_64 | 1.5.0 | 26.62 | 260.58 | 287.20 | OK | |
| r-release-macos-arm64 | 1.5.0 | OK | ||||
| r-release-macos-x86_64 | 1.5.0 | 15.00 | 259.00 | 274.00 | OK | |
| r-release-windows-x86_64 | 1.5.0 | 29.00 | 281.00 | 310.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.0 | OK | ||||
| r-oldrel-macos-x86_64 | 1.5.0 | 16.00 | 173.00 | 189.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.0 | 39.00 | 358.00 | 397.00 | OK |
Version: 1.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [45s/43s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(EMJMCMC)
Loading required package: bigmemory
>
> test_check("EMJMCMC")
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0.0655, 0.0925, 0.109, 0.1075, 0.18, 0.172, 0.1805, 0.182, 0.1985, 0.19, 0.275, 0.236, 0.275, 0.239, 0.301, 0.269, 0.35, 0.34, 0.355, 0.3245, 0.4545, 0.499, 0.1415, 0.174, 0.278, 0.35, 0.305, 0.072, 0.0935, 0.0935, 0.085, 0.1185, 0.145, 0.1305, 0.19, 0.169, 0.216, 0.206, 0.225, 0.19, 0.2095, 0.3095, 0.3, 0.31, 0.3, 0.2755, 0.298, 0.27, 0.37, 0.3705, 0.37, 0.35, 0.37, 0.355, 0.381, 0.37, 0.386, 0.48, 0.506, 0.475, 0.5385, 0.48, 0.126, 0.139, 0.3795, 0.1785, 0.209, 0.2785, 0.3315, 0.3865, 0.042, 0.077, 0.15, 0.15, 0.165, 0.185, 0.252, 0.26, 0.24, 0.315, 0.335, 0.32, 0.335, 0.347, 0.424, 0.43, 0.365, 0.405, 0.657, 0.385, 0.355, 0.03, 0.046, 0.041, 0.11, 0.0885, 0.115, 0.146, 0.155, 0.14, 0.259, 0.3, 0.2845, 0.079, 0.081, 0.088, 0.156, 0.1765, 0.2405, 0.229, 0.249, 0.308, 0.296, 0.495), y = c(`1` = 15L, `2` = 7L, `3` = 9L, `4` = 10L, `5` = 7L, `6` = 8L, `7` = 20L, `8` = 16L, `9` = 9L, `11` = 14L, `12` = 10L, `13` = 11L, `15` = 10L, `16` = 12L, `17` = 7L, `18` = 10L, `19` = 7L, `20` = 9L, `21` = 11L, `22` = 10L, `23` = 12L, `24` = 9L, `25` = 10L, `26` = 11L, `27` = 11L, `29` = 15L, `30` = 11L, `31` = 10L, `33` = 18L, `34` = 19L, `36` = 8L, `37` = 16L, `38` = 8L, `39` = 11L, `40` = 9L, `41` = 9L, `43` = 5L, `44` = 5L, `45` = 4L, `46` = 7L, `47` = 9L, `48` = 7L, `49` = 6L, `51` = 8L, `52` = 7L, `53` = 10L, `54` = 10L, `55` = 7L, `56` = 8L, `57` = 8L, `58` = 8L, `59` = 4L, `60` = 7L, `61` = 7L, `62` = 9L, `63` = 10L, `65` = 8L, `66` = 8L, `69` = 10L, `70` = 6L, `71` = 13L, `72` = 8L, `73` = 20L, `74` = 11L, `75` = 13L, `76` = 15L, `77` = 9L, `80` = 14L, `81` = 9L, `82` = 12L, `83` = 16L, `85` = 14L, `86` = 12L, `87` = 13L, `88` = 10L, `89` = 9L, `90` = 12L, `91` = 15L, `92` = 12L, `93` = 13L, `94` = 10L, `95` = 15L, `96` = 14L, `98` = 8L, `99` = 7L, `100` = 10L, `101` = 7L, `102` = 15L, `106` = 12L, `107` = 11L, `108` = 10L, `111` = 9L, `112` = 9L, `113` = 9L, `114` = 9L, `116` = 11L, `117` = 11L, `118` = 10L, `119` = 9L, `120` = 8L, `121` = 9L, `123` = 14L, `124` = 6L, `125` = 6L, `126` = 5L, `127` = 6L, `128` = 8L, `129` = 19L, `130` = 18L, `131` = 17L, `132` = 9L, `133` = 7L, `134` = 7L, `137` = 7L, `139` = 9L, `140` = 9L, `141` = 10L, `142` = 10L, `145` = 10L, `146` = 10L, `147` = 10L, `148` = 9L, `149` = 5L, `150` = 4L, `151` = 15L, `152` = 9L, `153` = 10L, `154` = 10L, `155` = 12L, `156` = 10L, `158` = 16L, `159` = 13L, `160` = 13L, `161` = 13L, `162` = 13L, `165` = 16L, `166` = 14L, `167` = 20L, `169` = 14L, `170` = 12L, `171` = 14L, `172` = 7L, `174` = 8L, `178` = 8L, `179` = 4L, `180` = 11L, `181` = 14L, `182` = 21L, `183` = 10L, `186` = 13L, `188` = 10L, `189` = 11L, `191` = 13L, `193` = 14L, `194` = 8L, `195` = 10L, `196` = 12L, `198` = 16L, `201` = 9L, `202` = 13L, `204` = 13L, `205` = 8L, `206` = 9L, `208` = 8L, `209` = 13L, `211` = 10L, `212` = 7L, `216` = 10L, `217` = 8L, `218` = 7L, `219` = 10L, `220` = 8L, `221` = 9L, `222` = 11L, `223` = 9L, `224` = 11L, `226` = 9L, `227` = 7L, `228` = 7L, `229` = 11L, `230` = 15L, `232` = 14L, `233` = 22L, `234` = 7L, `235` = 12L, `236` = 9L, `237` = 1L, `238` = 3L, `240` = 5L, `242` = 5L, `243` = 5L, `244` = 8L, `245` = 8L, `246` = 10L, `247` = 13L, `248` = 9L, `249` = 7L, `251` = 7L, `252` = 13L, `253` = 12L, `254` = 15L, `256` = 15L, `257` = 19L, `260` = 13L, `262` = 12L, `263` = 11L, `264` = 4L, `265` = 6L, `266` = 11L, `267` = 14L, `268` = 8L, `269` = 9L, `272` = 16L, `275` = 13L, `276` = 22L, `277` = 12L, `278` = 18L, `279` = 20L, `280` = 11L, `281` = 15L, `282` = 7L, `283` = 9L, `284` = 14L, `285` = 14L, `286` = 10L, `289` = 9L, `290` = 10L, `293` = 15L, `295` = 26L, `296` = 6L, `298` = 4L, `299` = 11L, `301` = 9L, `302` = 13L, `303` = 8L, `304` = 6L, `305` = 10L, `306` = 4L, `310` = 10L, `311` = 21L, `312` = 14L, `313` = 19L, `314` = 23L, `315` = 23L, `316` = 8L, `318` = 10L, `319` = 18L, `320` = 6L, `321` = 5L, `322` = 4L, `325` = 7L, `326` = 7L, `327` = 7L, `328` = 12L, `330` = 8L, `332` = 8L, `333` = 5L, `334` = 5L, `335` = 16L, `336` = 11L, `337` = 14L, `338` = 16L, `339` = 13L, `343` = 12L, `344` = 12L, `345` = 8L, `346` = 13L, `348` = 6L, `350` = 14L, `351` = 8L, `352` = 22L, `353` = 12L, `354` = 9L, `356` = 20L, `357` = 13L, `361` = 18L, `363` = 20L, `364` = 16L, `366` = 19L, `367` = 11L, `368` = 10L, `369` = 12L, `370` = 17L, `372` = 16L, `373` = 19L, `374` = 14L, `375` = 13L, `376` = 20L, `377` = 11L, `378` = 10L, `379` = 15L, `380` = 12L, `382` = 10L, `383` = 10L, `384` = 12L, `385` = 10L, `386` = 9L, `387` = 12L, `388` = 10L, `389` = 14L, `390` = 9L, `393` = 10L, `394` = 9L, `395` = 6L, `396` = 11L, `397` = 10L, `398` = 8L, `400` = 11L, `401` = 9L, `404` = 10L, `405` = 7L, `407` = 8L, `408` = 16L, `409` = 11L, `411` = 15L, `412` = 14L, `413` = 11L, `414` = 12L, `415` = 14L, `416` = 15L, `417` = 20L, `418` = 20L, `420` = 13L, `421` = 14L, `422` = 11L, `423` = 13L, `424` = 8L, `426` = 13L, `427` = 18L, `428` = 19L, `429` = 21L, `431` = 18L, `432` = 20L, `433` = 18L, `434` = 22L, `435` = 13L, `436` = 11L, `438` = 14L, `440` = 13L, `441` = 10L, `443` = 19L, `444` = 10L, `449` = 12L, `450` = 18L, `451` = 16L, `453` = 17L, `454` = 11L, `456` = 11L, `457` = 15L, `458` = 9L, `459` = 10L, `460` = 11L, `461` = 11L, `463` = 6L, `464` = 5L, `465` = 6L, `467` = 12L, `468` = 13L, `469` = 17L, `470` = 21L, `471` = 9L, `472` = 10L, `473` = 9L, `475` = 10L, `476` = 17L, `477` = 9L, `478` = 17L, `479` = 21L, `480` = 16L, `481` = 29L, `482` = 17L, `483` = 15L, `484` = 19L, `485` = 12L, `486` = 13L, `488` = 15L, `489` = 11L, `490` = 14L, `491` = 14L, `492` = 13L, `494` = 15L, `495` = 17L, `496` = 15L, `497` = 12L, `499` = 11L, `501` = 12L, `504` = 13L, `505` = 17L, `506` = 15L, `507` = 12L, `508` = 15L, `509` = 11L, `510` = 16L, `511` = 10L, `512` = 10L, `513` = 10L, `514` = 6L, `516` = 6L, `517` = 9L, `518` = 5L, `519` = 4L, `521` = 3L, `523` = 9L, `524` = 5L, `525` = 4L, `527` = 3L, `528` = 14L, `531` = 20L, `532` = 13L, `533` = 12L, `535` = 10L, `536` = 11L, `537` = 11L, `538` = 7L, `540` = 10L, `541` = 15L, `542` = 11L, `543` = 15L, `544` = 8L, `545` = 10L, `546` = 11L, `547` = 8L, `550` = 11L, `551` = 17L, `552` = 13L, `553` = 12L, `554` = 13L, `555` = 9L, `556` = 10L, `557` = 14L, `558` = 12L, `560` = 13L, `561` = 8L, `563` = 11L, `564` = 11L, `565` = 14L, `566` = 10L, `568` = 12L, `570` = 11L, `571` = 16L, `572` = 14L, `573` = 20L, `574` = 17L, `575` = 10L, `576` = 11L, `577` = 10L, `578` = 10L, `580` = 17L, `582` = 14L, `583` = 19L, `585` = 11L, `587` = 10L, `588` = 13L, `589` = 14L, `590` = 13L, `591` = 13L, `592` = 9L, `593` = 18L, `595` = 12L, `596` = 13L, `598` = 13L, `600` = 16L, `601` = 17L, `602` = 10L, `603` = 13L, `604` = 11L, `606` = 13L, `607` = 9L, `609` = 10L, `610` = 12L, `613` = 9L, `614` = 18L, `615` = 15L, `616` = 13L, `617` = 12L, `618` = 7L, `619` = 9L, `620` = 7L, `621` = 10L, `622` = 18L, `623` = 12L, `624` = 17L, `625` = 15L, `626` = 12L, `627` = 10L, `628` = 8L, `629` = 21L, `631` = 9L, `632` = 7L, `633` = 10L, `635` = 9L, `636` = 8L, `637` = 7L, `638` = 6L, `639` = 14L, `640` = 13L, `642` = 13L, `643` = 19L, `644` = 7L, `645` = 9L, `648` = 9L, `649` = 11L, `650` = 9L, `651` = 5L, `652` = 6L, `654` = 7L, `655` = 10L, `657` = 17L, `658` = 16L, `659` = 18L, `660` = 11L, `661` = 18L, `662` = 11L, `665` = 19L, `666` = 10L, `667` = 11L, `668` = 15L, `669` = 13L, `670` = 13L, `671` = 14L, `673` = 19L, `674` = 21L, `675` = 23L, `678` = 11L, `679` = 23L, `681` = 7L, `682` = 10L, `683` = 7L, `684` = 16L, `685` = 10L, `686` = 15L, `688` = 16L, `689` = 11L, `690` = 11L, `692` = 5L, `694` = 9L, `695` = 4L, `696` = 7L, `698` = 5L, `699` = 13L, `700` = 16L, `701` = 12L, `702` = 14L, `705` = 9L, `706` = 10L, `707` = 11L, `709` = 10L, `711` = 7L, `712` = 11L, `714` = 8L, `715` = 9L, `716` = 7L, `717` = 8L, `718` = 6L, `719` = 4L, `720` = 2L, `721` = 3L, `722` = 13L, `723` = 15L, `724` = 15L, `725` = 11L, `727` = 10L, `728` = 12L, `729` = 13L, `730` = 15L, `731` = 11L, `735` = 18L, `736` = 10L, `737` = 12L, `738` = 12L, `739` = 14L, `740` = 10L, `741` = 14L, `744` = 17L, `745` = 20L, `746` = 17L, `747` = 17L, `748` = 9L, `752` = 10L, `753` = 13L, `755` = 20L, `757` = 20L, `759` = 13L, `760` = 15L, `762` = 16L, `763` = 17L, `764` = 14L, `765` = 14L, `769` = 11L, `770` = 17L, `771` = 11L, `772` = 8L, `774` = 9L, `775` = 10L, `776` = 11L, `777` = 15L, `778` = 13L, `779` = 15L, `780` = 15L, `782` = 12L, `783` = 10L, `785` = 5L, `786` = 15L, `787` = 9L, `788` = 12L, `789` = 9L, `790` = 12L, `791` = 10L, `792` = 10L, `794` = 12L, `795` = 12L, `796` = 15L, `797` = 11L, `798` = 9L, `799` = 11L, `800` = 11L, `801` = 10L, `802` = 10L, `803` = 9L, `804` = 7L, `805` = 10L, `808` = 15L, `810` = 12L, `811` = 12L, `813` = 6L, `815` = 7L, `816` = 7L, `817` = 6L, `818` = 6L, `819` = 6L, `821` = 7L, `822` = 6L, `823` = 7L, `824` = 6L, `827` = 6L, `829` = 6L, `830` = 7L, `831` = 6L, `832` = 6L, `833` = 9L, `834` = 7L, `835` = 7L, `837` = 8L, `839` = 8L, `840` = 9L, `841` = 8L, `842` = 9L, `843` = 9L, `845` = 8L, `846` = 9L, `847` = 11L, `848` = 10L, `850` = 10L, `851` = 10L, `855` = 10L, `856` = 9L, `857` = 9L, `860` = 8L, `862` = 12L, `863` = 11L, `864` = 10L, `865` = 11L, `867` = 9L, `868` = 12L, `869` = 10L, `870` = 9L, `871` = 12L, `872` = 11L, `873` = 9L, `874` = 11L, `875` = 11L, `876` = 14L, `877` = 9L, `879` = 9L, `880` = 8L, `881` = 11L, `882` = 9L, `883` = 17L, `884` = 15L, `886` = 10L, `887` = 12L, `888` = 9L, `889` = 11L, `890` = 11L, `891` = 11L, `892` = 17L, `894` = 5L, `895` = 3L, `896` = 6L, `897` = 4L, `899` = 4L, `900` = 5L, `901` = 4L, `903` = 5L, `905` = 8L, `906` = 5L, `909` = 7L, `910` = 7L, `911` = 5L, `912` = 7L, `913` = 7L, `914` = 6L, `918` = 7L, `920` = 6L, `922` = 5L, `923` = 6L, `924` = 6L, `925` = 6L, `927` = 7L, `928` = 8L, `929` = 6L, `930` = 6L, `932` = 6L, `933` = 7L, `934` = 7L, `935` = 8L, `936` = 7L, `937` = 7L, `939` = 6L, `940` = 7L, `942` = 7L, `945` = 6L, `947` = 8L, `948` = 9L, `950` = 6L, `951` = 6L, `952` = 8L, `953` = 7L, `954` = 7L, `955` = 8L, `956` = 8L, `957` = 7L, `958` = 6L, `959` = 8L, `960` = 7L, `961` = 7L, `962` = 9L, `963` = 8L, `964` = 9L, `965` = 8L, `966` = 8L, `967` = 7L, `969` = 8L, `970` = 8L, `972` = 7L, `973` = 8L, `975` = 8L, `976` = 8L, `978` = 8L, `979` = 8L, `981` = 9L, `983` = 9L, `984` = 8L, `986` = 10L, `987` = 8L, `988` = 8L, `989` = 7L, `990` = 9L, `994` = 10L, `995` = 8L, `998` = 8L, `999` = 8L, `1000` = 11L, `1001` = 11L, `1002` = 9L, `1003` = 9L, `1005` = 9L, `1008` = 11L, `1009` = 10L, `1010` = 8L, `1011` = 11L, `1012` = 10L, `1013` = 10L, `1014` = 10L, `1015` = 9L, `1016` = 9L, `1017` = 8L, `1018` = 11L, `1019` = 8L, `1020` = 11L, `1021` = 10L, `1023` = 11L, `1025` = 10L, `1026` = 11L, `1027` = 8L, `1029` = 11L, `1030` = 10L, `1031` = 10L, `1032` = 9L, `1033` = 11L, `1034` = 10L, `1037` = 11L, `1038` = 9L, `1039` = 10L, `1040` = 6L, `1041` = 11L, `1042` = 10L, `1043` = 12L, `1044` = 12L, `1045` = 8L, `1047` = 10L, `1048` = 12L, `1049` = 11L, `1050` = 10L, `1051` = 11L, `1052` = 11L, `1053` = 12L, `1056` = 4L, `1057` = 4L, `1058` = 4L, `1059` = 4L, `1060` = 5L, `1061` = 7L, `1062` = 6L, `1064` = 5L, `1065` = 6L, `1066` = 6L, `1067` = 6L, `1068` = 5L, `1069` = 6L, `1071` = 6L, `1072` = 7L, `1073` = 6L, `1074` = 7L, `1076` = 6L, `1077` = 7L, `1079` = 6L, `1080` = 7L, `1081` = 7L, `1083` = 7L, `1084` = 7L, `1086` = 7L, `1089` = 6L, `1091` = 6L, `1093` = 7L, `1094` = 7L, `1095` = 6L, `1096` = 6L, `1097` = 7L, `1098` = 8L, `1099` = 7L, `1101` = 9L, `1102` = 8L, `1103` = 6L, `1104` = 7L, `1105` = 8L, `1106` = 6L, `1107` = 9L, `1108` = 8L, `1109` = 8L, `1110` = 9L, `1112` = 8L, `1113` = 7L, `1115` = 9L, `1117` = 8L, `1118` = 8L, `1120` = 10L, `1121` = 7L, `1122` = 9L, `1123` = 9L, `1124` = 8L, `1125` = 8L, `1126` = 10L, `1127` = 9L, `1128` = 8L, `1129` = 8L, `1130` = 8L, `1131` = 9L, `1133` = 9L, `1135` = 8L, `1138` = 10L, `1140` = 9L, `1141` = 9L, `1143` = 9L, `1144` = 9L, `1145` = 8L, `1146` = 9L, `1147` = 9L, `1148` = 9L, `1153` = 8L, `1154` = 9L, `1155` = 8L, `1156` = 8L, `1159` = 9L, `1160` = 10L, `1161` = 9L, `1162` = 8L, `1163` = 9L, `1164` = 9L, `1165` = 10L, `1166` = 9L, `1167` = 9L, `1168` = 8L, `1169` = 8L, `1170` = 8L, `1171` = 9L, `1172` = 8L, `1173` = 8L, `1174` = 10L, `1175` = 9L, `1176` = 10L, `1177` = 10L, `1178` = 10L, `1179` = 9L, `1180` = 11L, `1181` = 11L, `1182` = 10L, `1184` = 8L, `1185` = 9L, `1186` = 9L, `1187` = 14L, `1188` = 10L, `1189` = 9L, `1190` = 12L, `1191` = 9L, `1192` = 10L, `1194` = 12L, `1196` = 9L, `1197` = 11L, `1198` = 12L, `1199` = 10L, `1201` = 10L, `1202` = 8L, `1203` = 12L, `1205` = 11L, `1206` = 11L, `1207` = 11L, `1208` = 11L, `1209` = 10L, `1210` = 11L, `1211` = 6L, `1212` = 4L, `1213` = 5L, `1214` = 5L, `1215` = 6L, `1216` = 5L, `1217` = 5L, `1218` = 5L, `1219` = 5L, `1220` = 6L, `1221` = 7L, `1222` = 5L, `1223` = 7L, `1224` = 6L, `1225` = 6L, `1226` = 5L, `1227` = 6L, `1228` = 8L, `1229` = 4L, `1230` = 6L, `1231` = 7L, `1232` = 7L, `1233` = 7L, `1235` = 5L, `1236` = 7L, `1237` = 6L, `1238` = 6L, `1239` = 8L, `1240` = 7L, `1241` = 6L, `1242` = 8L, `1243` = 9L, `1244` = 8L, `1246` = 5L, `1247` = 7L, `1248` = 8L, `1250` = 5L, `1251` = 5L, `1252` = 6L, `1253` = 7L, `1254` = 6L, `1255` = 7L, `1256` = 7L, `1258` = 8L, `1260` = 7L, `1261` = 9L, `1262` = 9L, `1263` = 8L, `1264` = 8L, `1265` = 8L, `1266` = 7L, `1267` = 9L, `1268` = 8L, `1269` = 11L, `1270` = 8L, `1271` = 8L, `1272` = 8L, `1273` = 8L, `1274` = 8L, `1275` = 8L, `1276` = 9L, `1279` = 8L, `1280` = 7L, `1281` = 8L, `1283` = 9L, `1284` = 9L, `1286` = 7L, `1287` = 9L, `1289` = 7L, `1290` = 8L, `1291` = 7L, `1293` = 9L, `1294` = 10L, `1295` = 9L, `1299` = 10L, `1300` = 9L, `1303` = 8L, `1304` = 9L, `1306` = 10L, `1307` = 9L, `1309` = 9L, `1311` = 8L, `1312` = 11L, `1313` = 9L, `1314` = 9L, `1315` = 10L, `1316` = 9L, `1317` = 9L, `1318` = 10L, `1320` = 9L, `1321` = 9L, `1322` = 9L, `1323` = 10L, `1324` = 9L, `1325` = 11L, `1326` = 8L, `1327` = 8L, `1328` = 11L, `1330` = 8L, `1333` = 10L, `1334` = 9L, `1335` = 10L, `1336` = 9L, `1337` = 10L, `1338` = 10L, `1339` = 10L, `1340` = 8L, `1341` = 9L, `1342` = 10L, `1343` = 10L, `1344` = 10L, `1345` = 10L, `1346` = 10L, `1347` = 8L, `1350` = 10L, `1351` = 10L, `1352` = 12L, `1353` = 10L, `1354` = 11L, `1355` = 10L, `1356` = 11L, `1357` = 10L, `1358` = 11L, `1359` = 11L, `1360` = 10L, `1361` = 11L, `1363` = 12L, `1364` = 10L, `1366` = 10L, `1367` = 9L, `1368` = 10L, `1369` = 10L, `1370` = 10L, `1372` = 10L, `1373` = 12L, `1374` = 10L, `1377` = 10L, `1378` = 12L, `1379` = 10L, `1380` = 10L, `1381` = 9L, `1382` = 11L, `1383` = 9L, `1384` = 12L, `1385` = 9L, `1386` = 11L, `1387` = 12L, `1390` = 10L, `1391` = 9L, `1392` = 9L, `1393` = 10L, `1394` = 10L, `1395` = 15L, `1396` = 10L, `1397` = 11L, `1398` = 10L, `1399` = 11L, `1400` = 11L, `1402` = 11L, `1404` = 10L, `1405` = 10L, `1406` = 13L, `1407` = 10L, `1408` = 10L, `1409` = 10L, `1410` = 10L, `1411` = 10L, `1413` = 10L, `1414` = 13L, `1415` = 9L, `1416` = 11L, `1418` = 10L, `1419` = 11L, `1420` = 12L, `1421` = 11L, `1422` = 13L, `1423` = 12L, `1424` = 11L, `1425` = 11L, `1426` = 12L, `1427` = 9L, `1428` = 14L, `1429` = 14L, `1432` = 7L, `1434` = 6L, `1435` = 7L, `1437` = 5L, `1438` = 6L, `1439` = 7L, `1441` = 7L, `1442` = 6L, `1443` = 5L, `1445` = 6L, `1446` = 6L, `1447` = 5L, `1448` = 8L, `1449` = 9L, `1452` = 8L, `1453` = 9L, `1454` = 6L, `1457` = 7L, `1458` = 7L, `1459` = 7L, `1460` = 6L, `1461` = 8L, `1463` = 7L, `1464` = 9L, `1465` = 9L, `1466` = 8L, `1467` = 8L, `1469` = 8L, `1470` = 8L, `1471` = 9L, `1472` = 8L, `1473` = 9L, `1474` = 8L, `1475` = 9L, `1476` = 8L, `1477` = 9L, `1478` = 8L, `1479` = 9L, `1481` = 9L, `1482` = 9L, `1483` = 8L, `1484` = 8L, `1485` = 9L, `1486` = 8L, `1487` = 9L, `1488` = 8L, `1490` = 11L, `1491` = 9L, `1493` = 11L, `1494` = 8L, `1497` = 9L, `1499` = 11L, `1500` = 9L, `1501` = 10L, `1502` = 9L, `1503` = 11L, `1505` = 10L, `1506` = 8L, `1507` = 9L, `1508` = 10L, `1509` = 11L, `1511` = 10L, `1513` = 9L, `1514` = 8L, `1515` = 11L, `1516` = 11L, `1517` = 10L, `1518` = 10L, `1520` = 10L, `1522` = 12L, `1524` = 12L, `1525` = 11L, `1527` = 10L, `1528` = 12L, `1529` = 13L, `1530` = 10L, `1531` = 11L, `1533` = 5L, `1534` = 6L, `1536` = 6L, `1540` = 7L, `1541` = 8L, `1543` = 7L, `1545` = 6L, `1546` = 7L, `1547` = 7L, `1548` = 6L, `1549` = 8L, `1552` = 7L, `1556` = 8L, `1558` = 8L, `1559` = 7L, `1560` = 7L, `1563` = 7L, `1565` = 7L, `1566` = 7L, `1567` = 9L, `1570` = 8L, `1571` = 7L, `1575` = 8L, `1576` = 7L, `1577` = 8L, `1578` = 8L, `1579` = 8L, `1580` = 8L, `1582` = 8L, `1583` = 9L, `1584` = 7L, `1585` = 6L, `1587` = 10L, `1588` = 8L, `1589` = 8L, `1590` = 9L, `1592` = 8L, `1594` = 8L, `1596` = 9L, `1598` = 10L, `1599` = 9L, `1603` = 9L, `1604` = 9L, `1605` = 10L, `1606` = 9L, `1607` = 10L, `1608` = 8L, `1609` = 9L, `1610` = 8L, `1611` = 8L, `1613` = 9L, `1615` = 8L, `1616` = 8L, `1618` = 8L, `1619` = 11L, `1620` = 10L, `1621` = 8L, `1622` = 10L, `1623` = 9L, `1625` = 9L, `1626` = 10L, `1627` = 8L, `1628` = 9L, `1630` = 10L, `1631` = 9L, `1634` = 10L, `1635` = 10L, `1636` = 9L, `1637` = 10L, `1639` = 9L, `1641` = 8L, `1642` = 9L, `1643` = 13L, `1644` = 8L, `1645` = 10L, `1647` = 9L, `1648` = 10L, `1649` = 10L, `1650` = 10L, `1651` = 11L, `1652` = 8L, `1654` = 10L, `1655` = 8L, `1657` = 9L, `1658` = 9L, `1659` = 8L, `1660` = 10L, `1661` = 9L, `1662` = 10L, `1663` = 9L, `1665` = 9L, `1666` = 8L, `1667` = 9L, `1668` = 11L, `1669` = 10L, `1670` = 10L, `1671` = 12L, `1672` = 9L, `1673` = 10L, `1675` = 9L, `1678` = 9L, `1679` = 12L, `1682` = 11L, `1683` = 11L, `1684` = 12L, `1686` = 10L, `1687` = 12L, `1688` = 9L, `1689` = 11L, `1691` = 9L, `1692` = 12L, `1693` = 10L, `1694` = 9L, `1696` = 8L, `1697` = 11L, `1698` = 10L, `1699` = 11L, `1700` = 13L, `1701` = 12L, `1702` = 10L, `1703` = 9L, `1704` = 10L, `1705` = 10L, `1706` = 11L, `1707` = 10L, `1708` = 12L, `1709` = 14L, `1710` = 9L, `1712` = 9L, `1714` = 10L, `1716` = 9L, `1718` = 9L, `1720` = 11L, `1721` = 9L, `1722` = 10L, `1723` = 9L, `1725` = 11L, `1726` = 9L, `1727` = 12L, `1728` = 10L, `1729` = 14L, `1730` = 11L, `1731` = 11L, `1732` = 11L, `1733` = 12L, `1734` = 9L, `1735` = 12L, `1736` = 11L, `1737` = 13L, `1739` = 11L, `1740` = 13L, `1742` = 10L, `1743` = 11L, `1744` = 11L, `1745` = 11L, `1746` = 12L, `1747` = 10L, `1748` = 15L, `1750` = 11L, `1751` = 12L, `1753` = 11L, `1755` = 13L, `1756` = 7L, `1757` = 14L, `1758` = 11L, `1760` = 12L, `1761` = 11L, `1763` = 12L, `1764` = 12L, `1765` = 5L, `1767` = 7L, `1769` = 8L, `1770` = 7L, `1771` = 6L, `1772` = 8L, `1773` = 8L, `1774` = 10L, `1775` = 7L, `1777` = 9L, `1778` = 9L, `1779` = 8L, `1780` = 9L, `1781` = 9L, `1782` = 8L, `1784` = 7L, `1787` = 8L, `1789` = 10L, `1792` = 10L, `1794` = 10L, `1795` = 10L, `1797` = 10L, `1798` = 8L, `1800` = 9L, `1801` = 9L, `1802` = 10L, `1805` = 10L, `1807` = 11L, `1808` = 11L, `1809` = 10L, `1810` = 8L, `1811` = 9L, `1812` = 11L, `1815` = 10L, `1816` = 11L, `1817` = 10L, `1819` = 10L, `1822` = 9L, `1823` = 11L, `1824` = 11L, `1826` = 4L, `1827` = 7L, `1828` = 6L, `1830` = 6L, `1832` = 6L, `1833` = 8L, `1834` = 6L, `1835` = 7L, `1836` = 7L, `1837` = 6L, `1839` = 8L, `1840` = 11L, `1841` = 8L, `1843` = 7L, `1845` = 7L, `1846` = 8L, `1848` = 8L, `1849` = 9L, `1850` = 7L, `1851` = 8L, `1852` = 9L, `1853` = 8L, `1855` = 8L, `1856` = 9L, `1857` = 11L, `1858` = 8L, `1860` = 10L, `1861` = 10L, `1863` = 8L, `1864` = 9L, `1865` = 9L, `1866` = 7L, `1867` = 10L, `1868` = 9L, `1869` = 8L, `1870` = 9L, `1871` = 8L, `1872` = 9L, `1873` = 9L, `1874` = 9L, `1875` = 9L, `1879` = 10L, `1880` = 9L, `1881` = 10L, `1884` = 8L, `1885` = 7L, `1886` = 9L, `1887` = 9L, `1888` = 9L, `1889` = 11L, `1892` = 9L, `1895` = 11L, `1898` = 13L, `1899` = 9L, `1900` = 9L, `1901` = 9L, `1902` = 10L, `1903` = 11L, `1904` = 10L, `1906` = 10L, `1907` = 11L, `1908` = 9L, `1909` = 9L, `1910` = 9L, `1911` = 10L, `1912` = 10L, `1913` = 10L, `1915` = 10L, `1916` = 11L, `1917` = 11L, `1918` = 10L, `1919` = 11L, `1920` = 10L, `1921` = 9L, `1922` = 10L, `1923` = 8L, `1925` = 8L, `1927` = 11L, `1928` = 9L, `1929` = 12L, `1930` = 10L, `1932` = 9L, `1933` = 11L, `1934` = 15L, `1936` = 12L, `1937` = 11L, `1939` = 10L, `1940` = 11L, `1941` = 11L, `1942` = 11L, `1943` = 9L, `1944` = 11L, `1945` = 11L, `1946` = 11L, `1947` = 12L, `1948` = 12L, `1949` = 10L, `1950` = 11L, `1952` = 10L, `1953` = 10L, `1954` = 9L, `1956` = 12L, `1957` = 11L, `1958` = 16L, `1959` = 10L, `1960` = 13L, `1961` = 10L, `1963` = 10L, `1964` = 9L, `1965` = 10L, `1966` = 11L, `1971` = 11L, `1972` = 11L, `1973` = 12L, `1974` = 10L, `1976` = 11L, `1977` = 13L, `1979` = 12L, `1980` = 13L, `1981` = 10L, `1982` = 12L, `1985` = 11L, `1986` = 13L, `1987` = 4L, `1988` = 4L, `1989` = 5L, `1990` = 6L, `1991` = 7L, `1992` = 6L, `1993` = 5L, `1994` = 6L, `1995` = 6L, `1996` = 6L, `1997` = 7L, `1998` = 6L, `1999` = 7L, `2000` = 6L, `2001` = 9L, `2003` = 7L, `2004` = 6L, `2005` = 7L, `2006` = 7L, `2007` = 7L, `2010` = 8L, `2011` = 7L, `2012` = 8L, `2014` = 10L, `2015` = 10L, `2016` = 8L, `2017` = 9L, `2018` = 12L, `2020` = 9L, `2023` = 9L, `2024` = 9L, `2025` = 11L, `2026` = 10L, `2028` = 9L, `2029` = 10L, `2030` = 9L, `2031` = 10L, `2032` = 10L, `2033` = 12L, `2034` = 10L, `2035` = 14L, `2036` = 9L, `2037` = 5L, `2038` = 5L, `2039` = 5L, `2040` = 6L, `2041` = 8L, `2042` = 8L, `2047` = 7L, `2048` = 6L, `2049` = 9L, `2050` = 8L, `2051` = 8L, `2052` = 8L, `2053` = 8L, `2054` = 7L, `2055` = 7L, `2056` = 9L, `2057` = 7L, `2059` = 8L, `2061` = 9L, `2062` = 8L, `2063` = 7L, `2065` = 8L, `2066` = 8L, `2067` = 10L, `2068` = 9L, `2069` = 9L, `2070` = 9L, `2071` = 11L, `2072` = 9L, `2073` = 8L, `2075` = 11L, `2076` = 9L, `2078` = 8L, `2079` = 8L, `2080` = 9L, `2081` = 9L, `2082` = 8L, `2083` = 10L, `2086` = 10L, `2088` = 11L, `2089` = 12L, `2090` = 10L, `2091` = 17L, `2093` = 10L, `2094` = 11L, `2095` = 7L, `2096` = 7L, `2097` = 11L, `2099` = 8L, `2100` = 10L, `2101` = 10L, `2102` = 19L, `2103` = 9L, `2104` = 9L, `2105` = 11L, `2106` = 10L, `2107` = 14L, `2108` = 15L, `2109` = 27L, `2110` = 13L, `2111` = 5L, `2112` = 9L, `2113` = 8L, `2114` = 7L, `2115` = 4L, `2116` = 3L, `2117` = 6L, `2118` = 7L, `2120` = 9L, `2121` = 7L, `2123` = 11L, `2124` = 6L, `2125` = 7L, `2126` = 8L, `2128` = 7L, `2131` = 9L, `2134` = 9L, `2135` = 8L, `2136` = 12L, `2137` = 9L, `2139` = 16L, `2142` = 7L, `2144` = 10L, `2145` = 9L, `2146` = 9L, `2147` = 11L, `2149` = 8L, `2150` = 7L, `2151` = 10L, `2152` = 18L, `2154` = 5L, `2155` = 10L, `2156` = 16L, `2157` = 12L, `2158` = 17L, `2160` = 12L, `2161` = 19L, `2164` = 9L, `2166` = 9L, `2167` = 6L, `2168` = 5L, `2170` = 4L, `2171` = 7L, `2172` = 6L, `2173` = 5L, `2174` = 10L, `2176` = 12L, `2177` = 20L, `2178` = 14L, `2179` = 15L, `2180` = 14L, `2181` = 21L, `2182` = 16L, `2183` = 13L, `2184` = 6L, `2187` = 9L, `2188` = 14L, `2190` = 10L, `2192` = 15L, `2193` = 14L, `2194` = 6L, `2195` = 13L, `2196` = 5L, `2197` = 11L, `2199` = 6L, `2201` = 13L, `2203` = 19L, `2204` = 18L, `2205` = 7L, `2207` = 5L, `2209` = 16L, `2210` = 27L, `2211` = 18L, `2212` = 17L, `2214` = 17L, `2215` = 8L, `2217` = 7L, `2218` = 13L, `2219` = 14L, `2220` = 13L, `2221` = 8L, `2222` = 17L, `2223` = 13L, `2224` = 14L, `2225` = 9L, `2228` = 7L, `2232` = 9L, `2233` = 9L, `2234` = 17L, `2235` = 14L, `2236` = 13L, `2237` = 15L, `2238` = 17L, `2239` = 8L, `2240` = 8L, `2242` = 11L, `2243` = 7L, `2245` = 11L, `2247` = 10L, `2248` = 9L, `2249` = 6L, `2250` = 12L, `2252` = 13L, `2253` = 14L, `2254` = 11L, `2255` = 14L, `2256` = 12L, `2257` = 8L, `2258` = 13L, `2260` = 13L, `2261` = 10L, `2263` = 17L, `2265` = 14L, `2268` = 15L, `2269` = 13L, `2270` = 12L, `2271` = 18L, `2273` = 15L, `2276` = 20L, `2277` = 14L, `2278` = 19L, `2279` = 9L, `2280` = 10L, `2282` = 8L, `2283` = 10L, `2284` = 7L, `2285` = 9L, `2287` = 9L, `2288` = 7L, `2289` = 6L, `2293` = 9L, `2294` = 6L, `2295` = 12L, `2296` = 10L, `2297` = 13L, `2298` = 8L, `2299` = 7L, `2300` = 8L, `2301` = 10L, `2303` = 9L, `2304` = 11L, `2305` = 11L, `2306` = 23L, `2308` = 16L, `2309` = 11L, `2310` = 13L, `2311` = 13L, `2312` = 7L, `2313` = 8L, `2314` = 16L, `2315` = 14L, `2316` = 17L, `2317` = 13L, `2319` = 12L, `2320` = 15L, `2321` = 10L, `2323` = 12L, `2324` = 8L, `2325` = 17L, `2326` = 10L, `2327` = 11L, `2328` = 13L, `2329` = 15L, `2331` = 9L, `2332` = 15L, `2335` = 23L, `2336` = 23L, `2337` = 18L, `2339` = 17L, `2340` = 17L, `2341` = 11L, `2342` = 7L, `2345` = 21L, `2346` = 17L, `2347` = 13L, `2348` = 11L, `2349` = 16L, `2350` = 9L, `2351` = 12L, `2352` = 19L, `2353` = 18L, `2354` = 17L, `2355` = 11L, `2356` = 13L, `2358` = 17L, `2359` = 20L, `2360` = 13L, `2361` = 11L, `2362` = 12L, `2364` = 18L, `2365` = 15L, `2366` = 12L, `2367` = 19L, `2369` = 16L, `2370` = 12L, `2371` = 8L, `2372` = 3L, `2373` = 12L, `2374` = 12L, `2375` = 12L, `2376` = 10L, `2377` = 8L, `2378` = 12L, `2379` = 12L, `2380` = 10L, `2381` = 5L, `2385` = 11L, `2386` = 12L, `2387` = 14L, `2389` = 10L, `2390` = 11L, `2391` = 16L, `2392` = 10L, `2395` = 14L, `2396` = 14L, `2397` = 14L, `2398` = 17L, `2400` = 17L, `2401` = 13L, `2403` = 16L, `2404` = 10L, `2406` = 15L, `2407` = 10L, `2408` = 12L, `2410` = 8L, `2411` = 10L, `2412` = 9L, `2413` = 7L, `2414` = 12L, `2416` = 10L, `2420` = 10L, `2422` = 15L, `2423` = 12L, `2424` = 10L, `2425` = 6L, `2426` = 13L, `2428` = 6L, `2430` = 9L, `2431` = 11L, `2432` = 18L, `2435` = 16L, `2437` = 23L, `2438` = 9L, `2439` = 16L, `2444` = 11L, `2445` = 9L, `2446` = 12L, `2447` = 14L, `2448` = 6L, `2449` = 7L, `2450` = 9L, `2451` = 10L, `2453` = 9L, `2454` = 8L, `2456` = 6L, `2457` = 5L, `2460` = 20L, `2461` = 14L, `2462` = 13L, `2463` = 8L, `2464` = 17L, `2465` = 11L, `2466` = 11L, `2467` = 9L, `2468` = 16L, `2469` = 8L, `2470` = 13L, `2471` = 11L, `2472` = 20L, `2473` = 14L, `2476` = 12L, `2477` = 18L, `2478` = 13L, `2479` = 8L, `2480` = 14L, `2481` = 13L, `2483` = 12L, `2484` = 14L, `2486` = 8L, `2487` = 13L, `2488` = 11L, `2489` = 14L, `2490` = 15L, `2492` = 10L, `2493` = 11L, `2494` = 8L, `2495` = 9L, `2496` = 12L, `2497` = 15L, `2498` = 12L, `2499` = 15L, `2501` = 12L, `2502` = 9L, `2503` = 5L, `2504` = 6L, `2505` = 6L, `2506` = 6L, `2508` = 7L, `2509` = 6L, `2511` = 8L, `2512` = 6L, `2514` = 8L, `2515` = 8L, `2517` = 10L, `2518` = 7L, `2519` = 8L, `2520` = 7L, `2521` = 9L, `2523` = 11L, `2524` = 9L, `2525` = 11L, `2527` = 9L, `2528` = 10L, `2530` = 8L, `2531` = 9L, `2532` = 10L, `2534` = 10L, `2535` = 12L, `2536` = 11L, `2539` = 15L, `2541` = 13L, `2542` = 9L, `2544` = 10L, `2545` = 10L, `2546` = 4L, `2547` = 4L, `2548` = 5L, `2549` = 4L, `2550` = 5L, `2551` = 5L, `2552` = 6L, `2553` = 6L, `2558` = 7L, `2559` = 6L, `2560` = 7L, `2561` = 7L, `2562` = 6L, `2563` = 6L, `2564` = 7L, `2566` = 8L, `2567` = 7L, `2569` = 7L, `2570` = 6L, `2571` = 7L, `2572` = 9L, `2573` = 7L, `2574` = 8L, `2575` = 8L, `2576` = 7L, `2578` = 7L, `2579` = 9L, `2580` = 7L, `2581` = 8L, `2582` = 6L, `2583` = 6L, `2584` = 8L, `2585` = 9L, `2586` = 8L, `2588` = 9L, `2590` = 7L, `2591` = 8L, `2592` = 8L, `2593` = 7L, `2594` = 9L, `2595` = 9L, `2598` = 8L, `2599` = 9L, `2600` = 8L, `2601` = 10L, `2602` = 10L, `2603` = 8L, `2604` = 9L, `2605` = 10L, `2606` = 9L, `2607` = 10L, `2608` = 9L, `2610` = 11L, `2612` = 8L, `2613` = 11L, `2614` = 11L, `2616` = 12L, `2617` = 9L, `2618` = 9L, `2619` = 13L, `2621` = 10L, `2622` = 12L, `2623` = 13L, `2624` = 10L, `2626` = 10L, `2627` = 4L, `2628` = 5L, `2629` = 5L, `2630` = 6L, `2631` = 6L, `2632` = 6L, `2634` = 6L, `2636` = 7L, `2637` = 7L, `2638` = 7L, `2639` = 7L, `2640` = 6L, `2641` = 7L, `2644` = 8L, `2647` = 7L, `2648` = 8L, `2649` = 9L, `2650` = 7L, `2651` = 8L, `2652` = 8L, `2653` = 9L, `2656` = 7L, `2657` = 7L, `2658` = 9L, `2660` = 8L, `2661` = 8L, `2662` = 9L, `2664` = 9L, `2665` = 9L, `2666` = 8L, `2667` = 8L, `2668` = 7L, `2669` = 8L, `2670` = 8L, `2671` = 9L, `2672` = 8L, `2673` = 8L, `2674` = 9L, `2675` = 9L, `2676` = 10L, `2677` = 10L, `2678` = 9L, `2679` = 7L, `2680` = 9L, `2682` = 9L, `2683` = 10L, `2685` = 9L, `2687` = 11L, `2688` = 10L, `2689` = 8L, `2690` = 9L, `2691` = 9L, `2692` = 9L, `2693` = 9L, `2694` = 9L, `2695` = 10L, `2696` = 9L, `2698` = 10L, `2699` = 9L, `2700` = 11L, `2701` = 13L, `2702` = 13L, `2703` = 11L, `2704` = 11L, `2705` = 10L, `2706` = 13L, `2707` = 11L, `2708` = 9L, `2709` = 11L, `2710` = 12L, `2712` = 3L, `2715` = 5L, `2717` = 6L, `2718` = 6L, `2719` = 7L, `2720` = 5L, `2721` = 7L, `2723` = 7L, `2726` = 8L, `2727` = 7L, `2728` = 7L, `2729` = 8L, `2730` = 7L, `2731` = 6L, `2732` = 8L, `2733` = 8L, `2735` = 8L, `2736` = 8L, `2739` = 8L, `2740` = 7L, `2741` = 8L, `2742` = 8L, `2743` = 8L, `2744` = 7L, `2745` = 11L, `2746` = 8L, `2748` = 10L, `2749` = 9L, `2751` = 8L, `2754` = 8L, `2755` = 8L, `2756` = 10L, `2757` = 8L, `2758` = 9L, `2759` = 10L, `2760` = 8L, `2761` = 10L, `2762` = 10L, `2763` = 9L, `2764` = 10L, `2765` = 9L, `2766` = 11L, `2767` = 8L, `2768` = 10L, `2769` = 11L, `2770` = 11L, `2771` = 10L, `2773` = 10L, `2774` = 11L, `2775` = 9L, `2777` = 10L, `2778` = 9L, `2779` = 8L, `2781` = 10L, `2782` = 10L, `2783` = 8L, `2784` = 11L, `2785` = 9L, `2787` = 10L, `2788` = 11L, `2789` = 10L, `2790` = 9L, `2791` = 10L, `2792` = 10L, `2793` = 10L, `2794` = 12L, `2795` = 10L, `2796` = 12L, `2797` = 10L, `2798` = 11L, `2800` = 10L, `2803` = 9L, `2804` = 12L, `2805` = 9L, `2806` = 11L, `2807` = 9L, `2809` = 9L, `2810` = 11L, `2811` = 10L, `2812` = 9L, `2813` = 5L, `2814` = 4L, `2816` = 7L, `2817` = 6L, `2818` = 7L, `2819` = 8L, `2822` = 7L, `2824` = 7L, `2825` = 7L, `2828` = 8L, `2829` = 9L, `2830` = 7L, `2831` = 9L, `2832` = 7L, `2833` = 9L, `2835` = 8L, `2836` = 8L, `2837` = 9L, `2838` = 8L, `2839` = 9L, `2840` = 10L, `2841` = 9L, `2842` = 9L, `2843` = 9L, `2844` = 10L, `2845` = 10L, `2846` = 10L, `2847` = 10L, `2850` = 10L, `2851` = 10L, `2854` = 8L, `2856` = 11L, `2857` = 11L, `2858` = 11L, `2859` = 11L, `2860` = 11L, `2861` = 10L, `2863` = 10L, `2864` = 11L, `2865` = 6L, `2866` = 4L, `2867` = 5L, `2868` = 6L, `2872` = 7L, `2873` = 8L, `2874` = 6L, `2875` = 8L, `2876` = 7L, `2877` = 7L, `2878` = 9L, `2879` = 8L, `2880` = 8L, `2882` = 8L, `2884` = 8L, `2885` = 9L, `2886` = 8L, `2887` = 8L, `2888` = 9L, `2891` = 8L, `2893` = 10L, `2894` = 8L, `2895` = 11L, `2897` = 8L, `2898` = 10L, `2899` = 8L, `2900` = 8L, `2901` = 9L, `2902` = 8L, `2903` = 9L, `2904` = 8L, `2905` = 9L, `2907` = 8L, `2908` = 10L, `2910` = 9L, `2911` = 11L, `2912` = 11L, `2915` = 9L, `2916` = 10L, `2917` = 10L, `2919` = 9L, `2920` = 10L, `2921` = 10L, `2924` = 13L, `2926` = 10L, `2927` = 11L, `2928` = 12L, `2929` = 11L, `2930` = 11L, `2931` = 11L, `2934` = 10L, `2935` = 9L, `2936` = 10L, `2937` = 11L, `2938` = 10L, `2939` = 11L, `2941` = 10L, `2943` = 9L, `2944` = 10L, `2945` = 7L, `2946` = 11L, `2947` = 11L, `2948` = 10L, `2951` = 11L, `2954` = 11L, `2955` = 12L, `2956` = 15L, `2957` = 12L, `2958` = 11L, `2959` = 12L, `2961` = 10L, `2962` = 10L, `2964` = 12L, `2967` = 10L, `2968` = 11L, `2972` = 11L, `2974` = 12L, `2975` = 12L, `2976` = 6L, `2977` = 8L, `2978` = 6L, `2979` = 8L, `2980` = 8L, `2981` = 7L, `2982` = 8L, `2983` = 9L, `2984` = 8L, `2988` = 10L, `2989` = 9L, `2990` = 9L, `2992` = 8L, `2993` = 8L, `2996` = 9L, `2997` = 10L, `2999` = 9L, `3000` = 11L, `3001` = 9L, `3002` = 11L, `3004` = 10L, `3005` = 11L, `3006` = 13L, `3008` = 14L, `3011` = 6L, `3012` = 8L, `3014` = 8L, `3016` = 6L, `3017` = 7L, `3018` = 8L, `3019` = 9L, `3020` = 8L, `3021` = 8L, `3022` = 9L, `3023` = 8L, `3024` = 8L, `3025` = 9L, `3026` = 7L, `3027` = 8L, `3028` = 8L, `3029` = 7L, `3030` = 11L, `3031` = 11L, `3032` = 8L, `3033` = 9L, `3034` = 8L, `3035` = 10L, `3036` = 10L, `3037` = 11L, `3039` = 9L, `3041` = 10L, `3043` = 8L, `3044` = 10L, `3045` = 9L, `3047` = 11L, `3050` = 9L, `3051` = 12L, `3052` = 9L, `3053` = 9L, `3055` = 12L, `3056` = 11L, `3057` = 11L, `3059` = 11L, `3060` = 11L, `3061` = 11L, `3062` = 10L, `3063` = 11L, `3065` = 9L, `3066` = 11L, `3067` = 9L, `3068` = 11L, `3069` = 12L, `3070` = 11L, `3071` = 9L, `3072` = 11L, `3073` = 10L, `3074` = 10L, `3075` = 11L, `3076` = 11L, `3077` = 11L, `3078` = 10L, `3079` = 11L, `3082` = 14L, `3083` = 11L, `3084` = 4L, `3085` = 5L, `3086` = 6L, `3087` = 7L, `3088` = 7L, `3089` = 8L, `3092` = 10L, `3093` = 11L, `3094` = 11L, `3096` = 9L, `3097` = 9L, `3099` = 8L, `3101` = 10L, `3102` = 11L, `3103` = 8L, `3105` = 10L, `3106` = 5L, `3107` = 5L, `3108` = 5L, `3110` = 7L, `3111` = 8L, `3112` = 8L, `3113` = 7L, `3114` = 7L, `3115` = 7L, `3117` = 10L, `3119` = 8L, `3124` = 9L, `3126` = 10L, `3127` = 11L, `3128` = 9L, `3129` = 10L, `3131` = 11L, `3132` = 11L, `3133` = 10L, `3134` = 9L, `3135` = 8L, `3136` = 9L, `3137` = 11L, `3138` = 10L, `3139` = 10L, `3140` = 11L, `3141` = 20L, `3142` = 5L, `3143` = 5L, `3145` = 9L, `3146` = 13L, `3147` = 14L, `3148` = 13L, `3151` = 10L, `3152` = 21L, `3153` = 11L, `3155` = 7L, `3156` = 9L, `3158` = 9L, `3159` = 9L, `3161` = 7L, `3162` = 6L, `3163` = 15L, `3164` = 9L, `3165` = 13L, `3166` = 9L, `3168` = 15L, `3170` = 10L, `3171` = 14L, `3172` = 13L, `3175` = 9L, `3178` = 15L, `3179` = 8L, `3180` = 8L, `3182` = 11L, `3184` = 11L, `3187` = 6L, `3188` = 8L, `3189` = 16L, `3191` = 5L, `3192` = 13L, `3193` = 14L, `3194` = 20L, `3197` = 5L, `3198` = 7L, `3199` = 11L, `3200` = 9L, `3201` = 10L, `3202` = 6L, `3203` = 17L, `3204` = 17L, `3205` = 15L, `3207` = 8L, `3208` = 15L, `3211` = 12L, `3212` = 10L, `3213` = 14L, `3214` = 13L, `3215` = 14L, `3216` = 11L, `3218` = 10L, `3219` = 14L, `3221` = 11L, `3223` = 18L, `3224` = 8L, `3226` = 9L, `3227` = 10L, `3228` = 13L, `3229` = 9L, `3230` = 12L, `3231` = 11L, `3234` = 12L, `3236` = 14L, `3237` = 8L, `3238` = 18L, `3239` = 14L, `3240` = 15L, `3241` = 15L, `3242` = 14L, `3243` = 15L, `3244` = 14L, `3245` = 19L, `3246` = 13L, `3247` = 13L, `3248` = 15L, `3249` = 11L, `3250` = 6L, `3251` = 9L, `3252` = 8L, `3253` = 12L, `3254` = 6L, `3255` = 7L, `3256` = 5L, `3257` = 12L, `3258` = 9L, `3259` = 7L, `3260` = 18L, `3261` = 13L, `3262` = 12L, `3264` = 17L, `3265` = 12L, `3266` = 14L, `3267` = 11L, `3268` = 10L, `3269` = 11L, `3270` = 12L, `3271` = 13L, `3273` = 11L, `3274` = 13L, `3275` = 18L, `3276` = 12L, `3277` = 12L, `3278` = 15L, `3280` = 18L, `3281` = 24L, `3283` = 13L, `3288` = 12L, `3290` = 15L, `3291` = 12L, `3292` = 9L, `3295` = 14L, `3296` = 14L, `3297` = 14L, `3300` = 16L, `3301` = 16L, `3302` = 11L, `3303` = 15L, `3305` = 16L, `3306` = 16L, `3308` = 9L, `3309` = 5L, `3310` = 9L, `3311` = 13L, `3312` = 12L, `3313` = 17L, `3314` = 11L, `3315` = 11L, `3316` = 9L, `3318` = 7L, `3320` = 19L, `3321` = 16L, `3322` = 11L, `3324` = 12L, `3325` = 12L, `3326` = 10L, `3328` = 16L, `3329` = 13L, `3330` = 10L, `3331` = 10L, `3332` = 11L, `3333` = 13L, `3334` = 12L, `3336` = 12L, `3337` = 11L, `3338` = 15L, `3339` = 16L, `3340` = 12L, `3341` = 17L, `3342` = 12L, `3343` = 14L, `3344` = 13L, `3345` = 13L, `3346` = 12L, `3348` = 11L, `3349` = 13L, `3350` = 10L, `3351` = 11L, `3354` = 8L, `3355` = 10L, `3356` = 10L, `3359` = 5L, `3361` = 19L, `3363` = 9L, `3364` = 10L, `3365` = 11L, `3366` = 11L, `3368` = 17L, `3370` = 17L, `3371` = 12L, `3372` = 10L, `3374` = 19L, `3376` = 10L, `3377` = 6L, `3379` = 8L, `3380` = 8L, `3382` = 19L, `3383` = 15L, `3384` = 14L, `3386` = 13L, `3387` = 13L, `3388` = 18L, `3389` = 13L, `3390` = 16L, `3391` = 10L, `3392` = 13L, `3393` = 19L, `3394` = 10L, `3395` = 13L, `3397` = 18L, `3399` = 9L, `3400` = 8L, `3402` = 18L, `3403` = 13L, `3404` = 9L, `3405` = 8L, `3406` = 7L, `3407` = 8L, `3408` = 6L, `3409` = 7L, `3410` = 7L, `3411` = 6L, `3414` = 9L, `3415` = 7L, `3416` = 9L, `3418` = 9L, `3419` = 8L, `3420` = 8L, `3421` = 10L, `3422` = 11L, `3427` = 13L, `3428` = 13L, `3429` = 4L, `3430` = 6L, `3431` = 6L, `3434` = 6L, `3435` = 6L, `3437` = 6L, `3438` = 8L, `3439` = 6L, `3440` = 8L, `3441` = 7L, `3442` = 7L, `3443` = 8L, `3444` = 7L, `3447` = 8L, `3448` = 8L, `3449` = 7L, `3450` = 7L, `3451` = 7L, `3452` = 8L, `3453` = 8L, `3454` = 8L, `3455` = 8L, `3456` = 9L, `3457` = 12L, `3459` = 8L, `3460` = 8L, `3461` = 9L, `3462` = 11L, `3464` = 11L, `3465` = 10L, `3466` = 9L, `3468` = 10L, `3469` = 11L, `3470` = 10L, `3472` = 9L, `3476` = 6L, `3477` = 7L, `3478` = 6L, `3479` = 7L, `3482` = 6L, `3483` = 7L, `3484` = 7L, `3485` = 8L, `3486` = 7L, `3487` = 8L, `3489` = 8L, `3492` = 8L, `3493` = 8L, `3494` = 8L, `3495` = 9L, `3496` = 8L, `3497` = 8L, `3498` = 9L, `3499` = 10L, `3500` = 9L, `3501` = 8L, `3502` = 12L, `3503` = 10L, `3504` = 11L, `3505` = 10L, `3506` = 10L, `3507` = 9L, `3508` = 11L, `3509` = 9L, `3510` = 10L, `3511` = 9L, `3513` = 11L, `3514` = 9L, `3515` = 10L, `3516` = 12L, `3517` = 11L, `3518` = 11L, `3519` = 11L, `3520` = 10L, `3521` = 11L, `3524` = 4L, `3525` = 6L, `3526` = 6L, `3527` = 6L, `3529` = 6L, `3531` = 7L, `3532` = 7L, `3533` = 7L, `3534` = 6L, `3535` = 6L, `3536` = 9L, `3537` = 8L, `3538` = 6L, `3540` = 8L, `3541` = 8L, `3543` = 8L, `3544` = 8L, `3545` = 9L, `3547` = 7L, `3548` = 9L, `3550` = 9L, `3551` = 8L, `3552` = 9L, `3555` = 8L, `3556` = 8L, `3557` = 9L, `3558` = 8L, `3559` = 10L, `3560` = 10L, `3561` = 10L, `3562` = 9L, `3564` = 8L, `3565` = 9L, `3566` = 9L, `3567` = 9L, `3568` = 10L, `3570` = 11L, `3571` = 9L, `3572` = 9L, `3573` = 9L, `3574` = 10L, `3576` = 8L, `3578` = 11L, `3579` = 10L, `3580` = 9L, `3581` = 10L, `3582` = 9L, `3583` = 10L, `3584` = 9L, `3585` = 11L, `3586` = 9L, `3587` = 11L, `3589` = 12L, `3590` = 11L, `3591` = 11L, `3592` = 9L, `3593` = 11L, `3594` = 8L, `3595` = 12L, `3596` = 11L, `3597` = 10L, `3598` = 11L, `3599` = 12L, `3600` = 12L, `3601` = 4L, `3604` = 7L, `3606` = 8L, `3607` = 7L, `3609` = 7L, `3611` = 9L, `3612` = 10L, `3613` = 10L, `3614` = 11L, `3615` = 11L, `3617` = 14L, `3620` = 12L, `3621` = 9L, `3622` = 8L, `3623` = 10L, `3624` = 10L, `3626` = 10L, `3627` = 10L, `3628` = 10L, `3630` = 6L, `3631` = 7L, `3632` = 5L, `3633` = 5L, `3634` = 5L, `3635` = 6L, `3637` = 8L, `3638` = 6L, `3639` = 8L, `3640` = 8L, `3641` = 7L, `3642` = 9L, `3643` = 8L, `3644` = 8L, `3645` = 7L, `3646` = 9L, `3647` = 9L, `3648` = 8L, `3650` = 9L, `3652` = 10L, `3654` = 9L, `3655` = 9L, `3656` = 9L, `3657` = 11L, `3658` = 7L, `3660` = 9L, `3661` = 10L, `3662` = 10L, `3663` = 8L, `3664` = 9L, `3665` = 9L, `3668` = 11L, `3669` = 10L, `3670` = 9L, `3672` = 10L, `3673` = 10L, `3674` = 10L, `3675` = 12L, `3678` = 11L, `3679` = 10L, `3680` = 10L, `3681` = 9L, `3682` = 11L, `3683` = 11L, `3685` = 11L, `3686` = 10L, `3689` = 11L, `3692` = 12L, `3694` = 11L, `3696` = 10L, `3697` = 13L, `3698` = 12L, `3699` = 10L, `3700` = 11L, `3701` = 10L, `3703` = 11L, `3704` = 11L, `3705` = 11L, `3706` = 9L, `3707` = 11L, `3708` = 11L, `3709` = 9L, `3710` = 10L, `3711` = 11L, `3712` = 11L, `3713` = 10L, `3716` = 11L, `3717` = 5L, `3718` = 7L, `3720` = 7L, `3724` = 9L, `3725` = 7L, `3726` = 9L, `3727` = 8L, `3728` = 7L, `3729` = 10L, `3730` = 9L, `3731` = 9L, `3732` = 9L, `3733` = 10L, `3734` = 10L, `3735` = 10L, `3736` = 9L, `3738` = 9L, `3739` = 9L, `3741` = 11L, `3742` = 12L, `3743` = 13L, `3745` = 5L, `3748` = 6L, `3750` = 7L, `3751` = 8L, `3752` = 7L, `3754` = 8L, `3755` = 8L, `3756` = 8L, `3758` = 10L, `3760` = 9L, `3761` = 9L, `3762` = 10L, `3763` = 8L, `3764` = 11L, `3765` = 10L, `3767` = 10L, `3768` = 10L, `3769` = 9L, `3771` = 9L, `3773` = 9L, `3774` = 9L, `3776` = 8L, `3777` = 13L, `3779` = 9L, `3780` = 10L, `3781` = 10L, `3782` = 10L, `3785` = 11L, `3787` = 10L, `3788` = 12L, `3789` = 8L, `3791` = 10L, `3792` = 11L, `3793` = 13L, `3794` = 14L, `3795` = 10L, `3796` = 12L, `3797` = 11L, `3798` = 13L, `3800` = 11L, `3802` = 3L, `3803` = 6L, `3804` = 7L, `3805` = 7L, `3806` = 9L, `3807` = 8L, `3808` = 8L, `3809` = 10L, `3810` = 8L, `3812` = 9L, `3816` = 8L, `3817` = 8L, `3818` = 8L, `3819` = 9L, `3822` = 10L, `3823` = 9L, `3825` = 7L, `3826` = 11L, `3827` = 10L, `3828` = 11L, `3830` = 12L, `3832` = 10L, `3833` = 14L, `3835` = 6L, `3836` = 7L, `3837` = 9L, `3838` = 4L, `3839` = 8L, `3840` = 8L, `3841` = 9L, `3842` = 11L, `3843` = 9L, `3844` = 16L, `3845` = 14L, `3847` = 6L, `3848` = 9L, `3849` = 14L, `3851` = 7L, `3852` = 10L, `3854` = 9L, `3855` = 11L, `3857` = 9L, `3858` = 16L, `3860` = 9L, `3861` = 14L, `3862` = 14L, `3863` = 10L, `3864` = 18L, `3865` = 5L, `3866` = 19L, `3867` = 10L, `3868` = 15L, `3870` = 9L, `3871` = 15L, `3873` = 9L, `3874` = 5L, `3877` = 16L, `3878` = 15L, `3879` = 10L, `3880` = 15L, `3882` = 10L, `3884` = 10L, `3886` = 7L, `3890` = 12L, `3891` = 16L, `3892` = 12L, `3893` = 13L, `3894` = 9L, `3895` = 12L, `3896` = 13L, `3897` = 14L, `3898` = 17L, `3899` = 10L, `3900` = 4L, `3901` = 15L, `3902` = 12L, `3903` = 4L, `3905` = 16L, `3906` = 6L, `3907` = 4L, `3908` = 13L, `3909` = 9L, `3910` = 10L, `3913` = 15L, `3915` = 19L, `3917` = 10L, `3919` = 18L, `3920` = 11L, `3921` = 8L, `3922` = 10L, `3923` = 10L, `3924` = 12L, `3926` = 8L, `3929` = 10L, `3930` = 16L, `3932` = 17L, `3936` = 11L, `3937` = 13L, `3939` = 10L, `3940` = 14L, `3941` = 11L, `3942` = 15L, `3946` = 6L, `3948` = 16L, `3949` = 12L, `3950` = 13L, `3951` = 13L, `3954` = 8L, `3955` = 7L, `3956` = 9L, `3958` = 10L, `3960` = 11L, `3961` = 11L, `3962` = 10L, `3963` = 10L, `3964` = 4L, `3965` = 5L, `3966` = 6L, `3968` = 6L, `3971` = 6L, `3972` = 6L, `3973` = 8L, `3974` = 7L, `3976` = 6L, `3977` = 7L, `3979` = 8L, `3980` = 6L, `3981` = 7L, `3984` = 6L, `3986` = 11L, `3987` = 10L, `3988` = 11L, `3990` = 11L, `3991` = 10L, `3995` = 4L, `3996` = 5L, `3997` = 6L, `3998` = 6L, `3999` = 6L, `4000` = 6L, `4002` = 6L, `4005` = 10L, `4006` = 9L, `4007` = 9L, `4008` = 11L, `4009` = 12L, `4011` = 9L, `4014` = 8L, `4015` = 10L, `4016` = 10L, `4019` = 8L, `4020` = 10L, `4021` = 11L, `4022` = 10L, `4024` = 6L, `4027` = 7L, `4028` = 6L, `4029` = 8L, `4031` = 7L, `4032` = 8L, `4033` = 8L, `4034` = 10L, `4035` = 7L, `4036` = 11L, `4037` = 8L, `4038` = 10L, `4041` = 12L, `4043` = 10L, `4044` = 8L, `4045` = 10L, `4046` = 9L, `4047` = 11L, `4049` = 13L, `4050` = 9L, `4051` = 9L, `4053` = 13L, `4057` = 11L, `4058` = 11L, `4060` = 8L, `4061` = 8L, `4062` = 10L, `4064` = 11L, `4065` = 9L, `4066` = 6L, `4067` = 7L, `4068` = 6L, `4069` = 7L, `4070` = 8L, `4071` = 7L, `4073` = 8L, `4074` = 9L, `4075` = 8L, `4076` = 8L, `4078` = 9L, `4079` = 10L, `4080` = 8L, `4081` = 8L, `4083` = 9L, `4084` = 10L, `4085` = 10L, `4086` = 11L, `4088` = 10L, `4089` = 9L, `4090` = 9L, `4091` = 11L, `4092` = 12L, `4094` = 11L, `4096` = 11L, `4098` = 9L, `4099` = 9L, `4100` = 9L, `4101` = 9L, `4102` = 11L, `4103` = 11L, `4104` = 10L, `4105` = 11L, `4106` = 9L, `4107` = 11L, `4109` = 7L, `4110` = 8L, `4111` = 9L, `4112` = 8L, `4115` = 9L, `4117` = 9L, `4118` = 9L, `4119` = 9L, `4120` = 4L, `4121` = 7L, `4122` = 9L, `4123` = 8L, `4125` = 8L, `4126` = 9L, `4127` = 11L, `4128` = 10L, `4130` = 10L, `4133` = 10L, `4136` = 11L, `4137` = 9L, `4139` = 11L, `4140` = 10L, `4141` = 10L, `4142` = 11L, `4143` = 13L, `4144` = 13L, `4146` = 11L, `4147` = 10L, `4149` = 11L, `4150` = 6L, `4151` = 7L, `4152` = 6L, `4154` = 8L, `4155` = 6L, `4156` = 6L, `4157` = 8L, `4158` = 8L, `4159` = 8L, `4160` = 9L, `4161` = 11L, `4162` = 11L, `4164` = 7L, `4165` = 7L, `4166` = 7L, `4167` = 10L, `4169` = 8L, `4171` = 10L, `4172` = 8L, `4173` = 11L, `4175` = 9L, `4176` = 10L, `4177` = 12L), weights = NULL, start = NULL, etastart = NULL, mustart = NULL, offset = NULL, family = list(family = "gaussian", link = "identity", linkfun = function (mu) mu, linkinv = function (eta) eta, variance = function (mu) rep.int(1, length(mu)), dev.resids = function (y, mu, wt) wt * ((y - mu)^2), aic = function (y, n, mu, wt, dev) { nobs <- length(y) nobs * (log(dev/nobs * 2 * pi) + 1) + 2 - sum(log(wt)) }, mu.eta = function (eta) rep.int(1, length(eta)), initialize = expression({ n <- rep.int(1, nobs) if (is.null(etastart) && is.null(start) && is.null(mustart) && ((family$link == "inverse" && any(y == 0)) || (family$link == "log" && any(y <= 0)))) stop("cannot find valid starting values: please specify some") mustart <- y }), validmu = function (mu) TRUE, valideta = function (eta) TRUE, dispersion = NA_real_), control = list(epsilon = 1e-08, maxit = 25, trace = FALSE), intercept = TRUE, singular.ok = TRUE)
2: eval(call(if (is.function(method)) "method" else method, x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0L, singular.ok = singular.ok))
3: eval(call(if (is.function(method)) "method" else method, x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0L, singular.ok = singular.ok))
4: stats::glm(formula = formula, data = data, family = stats::gaussian)
5: withVisible(...elt(i))
6: utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out))}, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam)))})
7: tryCatchList(expr, classes, parentenv, handlers)
8: tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out))}, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam)))}))
9: (function (formula, data, r = 1/1000, logn = log(1000), relat = c("cos", "sigmoid", "tanh", "atan", "sin", "erf")) { fparam <- NULL fmla.proc <- as.character(formula)[2:3] fobserved <- fmla.proc[1] fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = " ", replacement = "") fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = "\n", replacement = "") fparam <- stringi::stri_split_fixed(str = fmla.proc[2], pattern = "+I", omit_empty = FALSE)[[1]] sj <- (stringi::stri_count_fixed(str = fparam, pattern = "*")) sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = "+")) for (rel in relat) { sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = rel)) } tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out)) }, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) })) return(list(mlik = mlik, waic = waic, dic = dic, summary.fixed = summary.fixed))})(data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 7L, 6L, 8L, 7L, 10L, 10L, 7L, 8L, 8L, 8L, 4L, 7L, 7L, 9L, 10L, 8L, 8L, 10L, 6L, 13L, 8L, 20L, 11L, 13L, 15L, 9L, 14L, 9L, 12L, 16L, 14L, 12L, 13L, 10L, 9L, 12L, 15L, 12L, 13L, 10L, 15L, 14L, 8L, 7L, 10L, 7L, 15L, 12L, 11L, 10L, 9L, 9L, 9L, 9L, 11L, 11L, 10L, 9L, 8L, 9L, 14L, 6L, 6L, 5L, 6L, 8L, 19L, 18L, 17L, 9L, 7L, 7L, 7L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 9L, 5L, 4L, 15L, 9L, 10L, 10L, 12L, 10L, 16L, 13L, 13L, 13L, 13L, 16L, 14L, 20L, 14L, 12L, 14L, 7L, 8L, 8L, 4L, 11L, 14L, 21L, 10L, 13L, 10L, 11L, 13L, 14L, 8L, 10L, 12L, 16L, 9L, 13L, 13L, 8L, 9L, 8L, 13L, 10L, 7L, 10L, 8L, 7L, 10L, 8L, 9L, 11L, 9L, 11L, 9L, 7L, 7L, 11L, 15L, 14L, 22L, 7L, 12L, 9L, 1L, 3L, 5L, 5L, 5L, 8L, 8L, 10L, 13L, 9L, 7L, 7L, 13L, 12L, 15L, 15L, 19L, 13L, 12L, 11L, 4L, 6L, 11L, 14L, 8L, 9L, 16L, 13L, 22L, 12L, 18L, 20L, 11L, 15L, 7L, 9L, 14L, 14L, 10L, 9L, 10L, 15L, 26L, 6L, 4L, 11L, 9L, 13L, 8L, 6L, 10L, 4L, 10L, 21L, 14L, 19L, 23L, 23L, 8L, 10L, 18L, 6L, 5L, 4L, 7L, 7L, 7L, 12L, 8L, 8L, 5L, 5L, 16L, 11L, 14L, 16L, 13L, 12L, 12L, 8L, 13L, 6L, 14L, 8L, 22L, 12L, 9L, 20L, 13L, 18L, 20L, 16L, 19L, 11L, 10L, 12L, 17L, 16L, 19L, 14L, 13L, 20L, 11L, 10L, 15L, 12L, 10L, 10L, 12L, 10L, 9L, 12L, 10L, 14L, 9L, 10L, 9L, 6L, 11L, 10L, 8L, 11L, 9L, 10L, 7L, 8L, 16L, 11L, 15L, 14L, 11L, 12L, 14L, 15L, 20L, 20L, 13L, 14L, 11L, 13L, 8L, 13L, 18L, 19L, 21L, 18L, 20L, 18L, 22L, 13L, 11L, 14L, 13L, 10L, 19L, 10L, 12L, 18L, 16L, 17L, 11L, 11L, 15L, 9L, 10L, 11L, 11L, 6L, 5L, 6L, 12L, 13L, 17L, 21L, 9L, 10L, 9L, 10L, 17L, 9L, 17L, 21L, 16L, 29L, 17L, 15L, 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0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 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0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L)), Age ~ 1 + I(Length) + I(Diameter) + I(Height) + I(WholeWeight) + I(ShuckedWeight) + I(VisceraWeight) + I(ShellWeight))
10: do.call(estimator, c(estimator.args, model$formula))
11: withVisible(...elt(i))
12: utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))})
13: tryCatchList(expr, classes, parentenv, handlers)
14: tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))}))
15: doWithOneRestart(return(expr), restart)
16: withOneRestart(expr, restarts[[1L]])
17: withRestarts(tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))})), abort = function() { onerr <- TRUE fm <- NULL})
18: withVisible(...elt(i))
19: utils::capture.output({ withRestarts(tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula)) })), abort = function() { onerr <- TRUE fm <- NULL })})
20: FUN(X[[i]], ...)
21: parallelize(X = vect, FUN = .self$fitmodel)
22: tryCatchList(expr, classes, parentenv, handlers)
23: tryCatch({ if (printable.opt) print(paste("proceed with layer", layer)) if (printable.opt) print(paste("current solution is", as.character(varcand))) vect <- buildmodel(max.cpu = layer, varcur.old = varcurb, statid = model$statid, switch.type = 5, min.N = min.N, max.N = max.N) if (printable.opt) print(paste("finish preparing models at layer", layer)) cluster <- TRUE flag1 <- 0 for (mod_id in 1:layer) { if (is.null(vect[[mod_id]]$formula)) { flag1 <- flag1 + 1 } } if (flag1 == layer) { cluster <- FALSE if (printable.opt) print("!!!!forward Models already estimated!!!!") } else { res.par <- parallelize(X = vect, FUN = .self$fitmodel) } if (printable.opt) print(paste("end forward optimizing at layer", layer)) for (mod_id in 1:layer) { if (cluster) { fmb <- fm fm <- res.par[[mod_id]] waiccand <- Inf if (is.null(fm) && (is.na(res.par[[mod_id]]$waic))) { varcand <- varcurb if (printable.opt) print("forward Model Fit Error!?") next } } varcand <- vect[[mod_id]]$varcur if (cluster) { waiccand <- res.par[[mod_id]]$waic mlikcand <- res.par[[mod_id]]$mlik } else if (exists("statistics1")) { iidd <- bittodec(varcand) + 1 waiccand <- statistics1[iidd, 2] mlikcand <- statistics1[iidd, 1] } else if (exists("hashStat")) { iidd <- paste(varcand, collapse = "") waiccand <- hash::values(hashStat[iidd])[2] mlikcand <- hash::values(hashStat[iidd])[1] } if (objective == 0) { objcand <- waiccand objcur <- waiccur objglob <- waicglob } else { objcand <- -mlikcand objcur <- -mlikcur objglob <- -mlikglob } if (objcand <= objcur || mod_id == 1) { if (printable.opt) print(paste("forward accept with ", objcand)) objcur <- objcand varcurb <- varcand waiccur <- waiccand varcur <- varcand mlikcur <- mlikcand if (objcur < objglob) { objglob <- objcur waicglob <- waiccand varglob <- varcand mlikglob <- mlikcand if (!is.null(fm)) { modglob <- fm } if (printable.opt) print(paste("forward global optima with ", objcand)) } } } if (objcur != objglob) { if (model$locstop) { break } else { varcurb <- varcur } }})
24: doWithOneRestart(return(expr), restart)
25: withOneRestart(expr, restarts[[1L]])
26: withRestarts(tryCatch({ if (printable.opt) print(paste("proceed with layer", layer)) if (printable.opt) print(paste("current solution is", as.character(varcand))) vect <- buildmodel(max.cpu = layer, varcur.old = varcurb, statid = model$statid, switch.type = 5, min.N = min.N, max.N = max.N) if (printable.opt) print(paste("finish preparing models at layer", layer)) cluster <- TRUE flag1 <- 0 for (mod_id in 1:layer) { if (is.null(vect[[mod_id]]$formula)) { flag1 <- flag1 + 1 } } if (flag1 == layer) { cluster <- FALSE if (printable.opt) print("!!!!forward Models already estimated!!!!") } else { res.par <- parallelize(X = vect, FUN = .self$fitmodel) } if (printable.opt) print(paste("end forward optimizing at layer", layer)) for (mod_id in 1:layer) { if (cluster) { fmb <- fm fm <- res.par[[mod_id]] waiccand <- Inf if (is.null(fm) && (is.na(res.par[[mod_id]]$waic))) { varcand <- varcurb if (printable.opt) print("forward Model Fit Error!?") next } } varcand <- vect[[mod_id]]$varcur if (cluster) { waiccand <- res.par[[mod_id]]$waic mlikcand <- res.par[[mod_id]]$mlik } else if (exists("statistics1")) { iidd <- bittodec(varcand) + 1 waiccand <- statistics1[iidd, 2] mlikcand <- statistics1[iidd, 1] } else if (exists("hashStat")) { iidd <- paste(varcand, collapse = "") waiccand <- hash::values(hashStat[iidd])[2] mlikcand <- hash::values(hashStat[iidd])[1] } if (objective == 0) { objcand <- waiccand objcur <- waiccur objglob <- waicglob } else { objcand <- -mlikcand objcur <- -mlikcur objglob <- -mlikglob } if (objcand <= objcur || mod_id == 1) { if (printable.opt) print(paste("forward accept with ", objcand)) objcur <- objcand varcurb <- varcand waiccur <- waiccand varcur <- varcand mlikcur <- mlikcand if (objcur < objglob) { objglob <- objcur waicglob <- waiccand varglob <- varcand mlikglob <- mlikcand if (!is.null(fm)) { modglob <- fm } if (printable.opt) print(paste("forward global optima with ", objcand)) } } } if (objcur != objglob) { if (model$locstop) { break } else { varcurb <- varcur } }}), abort = function() { varcur <- varcurb fm <- fmb closeAllConnections() withr::local_options(error = traceback) onerr <- TRUE})
27: forward_selection(list(varcur = rep(0, length(fparam.example)), mlikcur = -Inf, waiccur = Inf, locstop = glob.model$locstop, statid = -1))
28: mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models * 100, trest = n.models, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq))
29: (function (formula, data, secondary = vector(mode = "character", length = 0), latnames = "", estimator, estimator.args = "list", n.models, p.add.default = 1, p.add = 0.5, unique = FALSE, save.beta = FALSE, locstop.nd = FALSE, latent = "", max.cpu = 4, max.cpu.glob = 2, create.table = TRUE, hash.length = 20, presearch = TRUE, locstop = FALSE, pseudo.paral = FALSE, interact = FALSE, deep.method = 1, relations = c("", "sin", "cos", "sigmoid", "tanh", "atan", "erf"), relations.prob = c(0.4, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1), gen.prob = c(1, 10, 5, 1, 0), pool.cross = 0.9, p.epsilon = 1e-04, del.sigma = 0.5, pool.cor.prob = FALSE, interact.param = list(allow_offsprings = 2, mutation_rate = 100, last.mutation = 2000, max.tree.size = 10000, Nvars.max = 100, p.allow.replace = 0.7, p.allow.tree = 0.1, p.nor = 0.3, p.and = 0.7), prand = 0.01, keep.origin = TRUE, sup.large.n = 5000, recalc_margin = 2^10, create.hash = FALSE, interact.order = 1, burn.in = 1, eps = 10^6, max.time = 120, max.it = 25000, print.freq = 100, outgraphs = FALSE, advanced.param = NULL, distrib_of_neighbourhoods = t(array(data = c(7.6651604, 16.773326, 14.541629, 12.839445, 2.964227, 13.048343, 7.165434, 0.9936905, 15.94249, 11.040131, 3.200394, 15.349051, 5.466632, 14.676458, 1.5184551, 9.285762, 6.125034, 3.627547, 13.343413, 2.923767, 15.318774, 14.529538, 1.52196, 11.804457, 5.070282, 6.93438, 10.578945, 12.455602, 6.0826035, 2.453729, 14.340435, 14.863495, 1.028312, 12.685017, 13.806295), dim = c(7, 5))), distrib_of_proposals = c(76.9187, 71.25264, 87.68184, 60.55921, 15812.39852), quiet = TRUE) { global_env <- as.environment(1L) assign("data.example", data, envir = global_env) variables <- simplify.formula(formula, names(data.example)) assign("fparam.example", variables$fparam, envir = global_env) assign("fobserved.example", variables$fobserved, envir = global_env) fparam.tmp <- as.vector(sapply(FUN = paste, "I(", variables$fparam, ")", sep = "")[, 1]) if (latnames[1] != "") assign("fparam.example", c(fparam.tmp, latnames), envir = global_env) else assign("fparam.example", fparam.tmp, envir = global_env) assign("mySearch", methods::new(structure("EMJMCMC2016", package = "EMJMCMC")), envir = global_env) if (length(secondary) > 0) mySearch$filtered <- sapply(FUN = paste, "I(", secondary, ")", sep = "") mySearch$estimator <- estimator mySearch$latnames <- latnames mySearch$estimator.args <<- estimator.args mySearch$latent.formula <- latent mySearch$save.beta <- save.beta mySearch$prand <- prand mySearch$p.add <- array(p.add, length(fparam.example)) mySearch$p.add.default <- p.add.default mySearch$recalc.margin <<- as.integer(recalc_margin) mySearch$max.cpu <<- as.integer(max.cpu) mySearch$locstop.nd <- locstop.nd mySearch$pool.cor.prob <- pool.cor.prob mySearch$sup.large.n <- as.integer(sup.large.n) mySearch$max.cpu.glob <<- as.integer(max.cpu.glob) mySearch$deep.method <- as.integer(deep.method) if (interact) { mySearch$allow_offsprings <<- as.integer(interact.param$allow_offsprings) mySearch$mutation_rate <<- as.integer(interact.param$mutation_rate) mySearch$Nvars.max <<- as.integer(interact.param$Nvars.max) mySearch$max.tree.size <- as.integer(interact.param$max.tree.size) mySearch$p.allow.replace <- interact.param$p.allow.replace mySearch$p.allow.tree <<- interact.param$p.allow.tree mySearch$p.epsilon <- p.epsilon mySearch$keep.origin <- keep.origin mySearch$sigmas <<- relations mySearch$sigmas.prob <<- relations.prob mySearch$del.sigma <- del.sigma mySearch$pool.cross <- pool.cross mySearch$gen.prob <<- gen.prob mySearch$p.nor <- interact.param$p.nor mySearch$p.and <- interact.param$p.and mySearch$last.mutation <- as.integer(interact.param$last.mutation) } if (!is.null(advanced.param)) { mySearch$max.N.glob <<- as.integer(advanced.param$max.N.glob) mySearch$min.N.glob <<- as.integer(advanced.param$min.N.glob) mySearch$max.N <<- as.integer(advanced.param$max.N) mySearch$min.N <- as.integer(advanced.param$min.N) mySearch$printable.opt <- advanced.param$printable } if (exists("hashStat")) { hash::clear(hashStat) remove(hashStat, envir = global_env) } if (exists("statistics1")) { remove(statistics, envir = global_env) remove(statistics1, envir = global_env) } if (exists("hash.keys1")) { remove(hash.keys, envir = global_env) remove(hash.keys1, envir = global_env) } if (create.table) { if (pseudo.paral) mySearch$parallelize <- lapply assign("statistics1", bigmemory::big.matrix(nrow = 2^min((length(fparam.example)), hash.length) + 1, ncol = 16 + length(fparam.example) * save.beta, init = NA, type = "double"), envir = global_env) assign("statistics", bigmemory::describe(statistics1), envir = global_env) mySearch$g.results[4, 1] <- 0 mySearch$g.results[4, 2] <- 0 mySearch$p.add <- array(data = 0.5, dim = length(fparam.example)) if ((length(fparam.example)) > 20) { mySearch$hash.length <- as.integer(hash.length) mySearch$double.hashing <- TRUE assign(hash.keys1, bigmemory::big.matrix(nrow = 2^(hash.length) + 1, ncol = length(fparam.example), init = 0, type = "char"), global_env) assign(hash.keys, bigmemory::describe(hash.keys1), global_env) } } else if (create.hash) { assign("hashStat", hash::hash(), envir = global_env) mySearch$parallelize <<- lapply mySearch$hash.length <- as.integer(20) mySearch$double.hashing <<- FALSE } initsol = stats::rbinom(n = length(fparam.example), size = 1, prob = 0.5) if (unique) resm <- mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models * 100, trest = n.models, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq)) else resm <- mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models, trest = n.models * 100, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq)) ppp <- 1 if (!quiet) message("MJMCMC is completed") if (create.table) { ppp <- mySearch$post_proceed_results(statistics1 = statistics1) truth = ppp$p.post truth.m = ppp$m.post truth.prob = ppp$s.mass ordering = sort(ppp$p.post, index.return = TRUE) } else if (create.hash) { ppp <- mySearch$post_proceed_results_hash(hashStat = hashStat) truth = ppp$p.post truth.m = ppp$m.post truth.prob = ppp$s.mass ordering = sort(ppp$p.post, index.return = TRUE) } if (!quiet) { message("Post Proceed Results") cat("pi truth", sprintf("%.10f", truth[ordering$ix]), sprintf(fparam.example[ordering$ix]), sep = "\n") } if (outgraphs) { withr::local_par(mar = c(10, 4, 4, 2) + 4.1) graphics::barplot(resm$bayes.results$p.post, density = 46, border = "black", main = "Marginal Inclusion (RM)", ylab = "Probability", names.arg = mySearch$fparam, las = 2) graphics::barplot(resm$p.post, density = 46, border = "black", main = "Marginal Inclusion (MC)", ylab = "Probability", names.arg = mySearch$fparam, las = 2) } return(ppp)})(formula = Age ~ 1 + Length + Diameter + Height + WholeWeight + ShuckedWeight + VisceraWeight + ShellWeight + Male + Femele, data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 7L, 6L, 8L, 7L, 10L, 10L, 7L, 8L, 8L, 8L, 4L, 7L, 7L, 9L, 10L, 8L, 8L, 10L, 6L, 13L, 8L, 20L, 11L, 13L, 15L, 9L, 14L, 9L, 12L, 16L, 14L, 12L, 13L, 10L, 9L, 12L, 15L, 12L, 13L, 10L, 15L, 14L, 8L, 7L, 10L, 7L, 15L, 12L, 11L, 10L, 9L, 9L, 9L, 9L, 11L, 11L, 10L, 9L, 8L, 9L, 14L, 6L, 6L, 5L, 6L, 8L, 19L, 18L, 17L, 9L, 7L, 7L, 7L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 9L, 5L, 4L, 15L, 9L, 10L, 10L, 12L, 10L, 16L, 13L, 13L, 13L, 13L, 16L, 14L, 20L, 14L, 12L, 14L, 7L, 8L, 8L, 4L, 11L, 14L, 21L, 10L, 13L, 10L, 11L, 13L, 14L, 8L, 10L, 12L, 16L, 9L, 13L, 13L, 8L, 9L, 8L, 13L, 10L, 7L, 10L, 8L, 7L, 10L, 8L, 9L, 11L, 9L, 11L, 9L, 7L, 7L, 11L, 15L, 14L, 22L, 7L, 12L, 9L, 1L, 3L, 5L, 5L, 5L, 8L, 8L, 10L, 13L, 9L, 7L, 7L, 13L, 12L, 15L, 15L, 19L, 13L, 12L, 11L, 4L, 6L, 11L, 14L, 8L, 9L, 16L, 13L, 22L, 12L, 18L, 20L, 11L, 15L, 7L, 9L, 14L, 14L, 10L, 9L, 10L, 15L, 26L, 6L, 4L, 11L, 9L, 13L, 8L, 6L, 10L, 4L, 10L, 21L, 14L, 19L, 23L, 23L, 8L, 10L, 18L, 6L, 5L, 4L, 7L, 7L, 7L, 12L, 8L, 8L, 5L, 5L, 16L, 11L, 14L, 16L, 13L, 12L, 12L, 8L, 13L, 6L, 14L, 8L, 22L, 12L, 9L, 20L, 13L, 18L, 20L, 16L, 19L, 11L, 10L, 12L, 17L, 16L, 19L, 14L, 13L, 20L, 11L, 10L, 15L, 12L, 10L, 10L, 12L, 10L, 9L, 12L, 10L, 14L, 9L, 10L, 9L, 6L, 11L, 10L, 8L, 11L, 9L, 10L, 7L, 8L, 16L, 11L, 15L, 14L, 11L, 12L, 14L, 15L, 20L, 20L, 13L, 14L, 11L, 13L, 8L, 13L, 18L, 19L, 21L, 18L, 20L, 18L, 22L, 13L, 11L, 14L, 13L, 10L, 19L, 10L, 12L, 18L, 16L, 17L, 11L, 11L, 15L, 9L, 10L, 11L, 11L, 6L, 5L, 6L, 12L, 13L, 17L, 21L, 9L, 10L, 9L, 10L, 17L, 9L, 17L, 21L, 16L, 29L, 17L, 15L, 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Traceback:
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0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1.0095, 0.8775, 1.068, 0.869, 0.907, 1.0225, 0.847, 1.114, 1.859, 0.814, 0.9615, 0.9955, 1.0265, 1.1915, 1.1025, 1.1175, 1.1835, 1.056, 1.0145, 1.3305, 1.052, 1.0565, 0.923, 1.027, 1.0195, 0.858, 1.1125, 1.3745, 1.2065, 1.1325, 1.2645, 1.1565, 1.369, 1.39, 1.1345, 1.464, 1.202, 1.31, 1.415, 1.363, 1.2975, 1.3645, 1.7025, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L)), estimator = function (formula, data, r = 1/1000, logn = log(1000), relat = c("cos", "sigmoid", "tanh", "atan", "sin", "erf")) { fparam <- NULL fmla.proc <- as.character(formula)[2:3] fobserved <- fmla.proc[1] fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = " ", replacement = "") fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = "\n", replacement = "") fparam <- stringi::stri_split_fixed(str = fmla.proc[2], 1.519, 1.454, 1.583, 1.5535, 1.74, 2.273, 1.219, 1.652, 2.0045, 1.6755, 1.9965, 1.187, 1.978, 2.124, 1.931, 2.236, 2.505, 2.0065, 2.657, 0.4645, 0.0765, 0.0685, 0.0835, 0.1125, 0.1075, 0.1055, pattern = "+I", omit_empty = FALSE)[[1]] sj <- (stringi::stri_count_fixed(str = fparam, pattern = "*")) sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = "+"))0.1375, 0.1145, 0.139, 0.2085, 0.1595, 0.2015, 0.179, 0.1945, 0.2005, 0.215, 0.1785, 0.196, 0.1875, 0.196, 0.213, 0.2445, 0.213, 0.2385, 0.218, 0.2465, 0.2025, 0.218, 0.256, 0.2105, 0.2615, for (rel in relat) { sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = rel)) } tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 0.2175, 0.228, 0.3265, 0.284, 0.2985, 0.2675, 0.2645, 0.33, 0.328, 0.3335, 0.3055, 0.428, 0.3685, 0.438, 0.448, 0.4005, 0.4305, 1000)/2 waic <- (out$deviance + 2 * out$rank) + 100000.372, 0.466, 0.3705, 0.4785, 0.346, 0.411, 0.48, 0.393, 0.5035, 0.441, 0.492, 0.5435, 0.4965, 0.5125, 0.553, 0.6765, 0.5795, 0.5695, 0.6215, 0.594, 0.551, 0.625, 0.6965, 0.5825, 0.663, 0.81, 0.5745, 0.5765, 0.7145, 0.8655, 0.7265, 0.8, 0.5945, 0.7895, 0.651, 0.8745, 0.7535, 0.7305, 0.8705, 0.7395, 0.7905, 0.8205, dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out)) }, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) })) return(list(mlik = mlik, waic = waic, dic = dic, summary.fixed = summary.fixed)) }, estimator.args = list(data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 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18L, 13L, 9L, 8L, 7L, 8L, 6L, 7L, 7L, 6L, 9L, 7L, 9L, 9L, 8L, 8L, 10L, 11L, 13L, 13L, 4L, 6L, 6L, 6L, 6L, 6L, 8L, 6L, 8L, 7L, 7L, 8L, 7L, 8L, 8L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 12L, 8L, 8L, 9L, 11L, 11L, 10L, 9L, 10L, 11L, 10L, 9L, 6L, 7L, 6L, 7L, 6L, 7L, 7L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 8L, 8L, 9L, 10L, 9L, 8L, 12L, 10L, 11L, 10L, 10L, 9L, 11L, 9L, 10L, 9L, 11L, 9L, 10L, 12L, 11L, 11L, 11L, 10L, 11L, 4L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 6L, 6L, 9L, 8L, 6L, 8L, 8L, 8L, 8L, 9L, 7L, 9L, 9L, 8L, 9L, 8L, 8L, 9L, 8L, 10L, 10L, 10L, 9L, 8L, 9L, 9L, 9L, 10L, 11L, 9L, 9L, 9L, 10L, 8L, 11L, 10L, 9L, 10L, 9L, 10L, 9L, 11L, 9L, 11L, 12L, 11L, 11L, 9L, 11L, 8L, 12L, 11L, 10L, 11L, 12L, 12L, 4L, 7L, 8L, 7L, 7L, 9L, 10L, 10L, 11L, 11L, 14L, 12L, 9L, 8L, 10L, 10L, 10L, 10L, 10L, 6L, 7L, 5L, 5L, 5L, 6L, 8L, 6L, 8L, 8L, 7L, 9L, 8L, 8L, 7L, 9L, 9L, 8L, 9L, 10L, 9L, 9L, 9L, 11L, 7L, 9L, 10L, 10L, 8L, 9L, 9L, 11L, 10L, 9L, 10L, 10L, 10L, 12L, 11L, 10L, 10L, 9L, 11L, 11L, 11L, 10L, 11L, 12L, 11L, 10L, 13L, 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9L, 14L, 14L, 10L, 18L, 5L, 19L, 10L, 15L, 9L, 15L, 9L, 5L, 16L, 15L, 10L, 15L, 10L, 10L, 7L, 12L, 16L, 12L, 13L, 9L, 12L, 13L, 14L, 17L, 10L, 4L, 15L, 12L, 4L, 16L, 6L, 4L, 13L, 9L, 10L, 15L, 19L, 10L, 18L, 11L, 8L, 10L, 10L, 12L, 8L, 10L, 16L, 17L, 11L, 13L, 10L, 14L, 11L, 15L, 6L, 16L, 12L, 13L, 13L, 8L, 7L, 9L, 10L, 11L, 11L, 10L, 10L, 4L, 5L, 6L, 6L, 6L, 6L, 8L, 7L, 6L, 7L, 8L, 6L, 7L, 6L, 11L, 10L, 11L, 11L, 10L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 10L, 9L, 9L, 11L, 12L, 9L, 8L, 10L, 10L, 8L, 10L, 11L, 10L, 6L, 7L, 6L, 8L, 7L, 8L, 8L, 10L, 7L, 11L, 8L, 10L, 12L, 10L, 8L, 10L, 9L, 11L, 13L, 9L, 9L, 13L, 11L, 11L, 8L, 8L, 10L, 11L, 9L, 6L, 7L, 6L, 7L, 8L, 7L, 8L, 9L, 8L, 8L, 9L, 10L, 2.1505, 0.118, 0.099, 0.1365, 0.164, 0.248, 0.252, 0.4015, 0.3905, 0.4095, 0.494, 0.599, 0.6245, 0.7745, 0.874, 1.21, 1.37, 1.759, 1.8065, 1.5945, 0.032, 0.0785, 0.134, 0.2075, 0.2235, 0.34, 0.635, 0.5815, 0.7545, 0.98, 1.0035, 1.133, 1.142, 1.131, 1.065, 1.181, 1.2275, 1.4415, 1.473, 1.658, 0.096, 0.196, 0.2155, 0.2805, 0.366, 8L, 8L, 9L, 10L, 10L, 11L, 10L, 9L, 9L, 11L, 12L, 11L, 11L, 9L, 9L, 9L, 9L, 11L, 11L, 10L, 11L, 9L, 11L, 7L, 8L, 9L, 8L, 9L, 9L, 9L, 9L, 4L, 7L, 9L, 8L, 8L, 9L, 11L, 10L, 10L, 10L, 11L, 9L, 11L, 10L, 10L, 11L, 13L, 13L, 11L, 10L, 11L, 6L, 7L, 6L, 8L, 6L, 6L, 8L, 8L, 8L, 9L, 11L, 11L, 7L, 7L, 7L, 10L, 8L, 10L, 8L, 11L, 9L, 10L, 12L), Length = c(0.455, 0.35, 0.53, 0.44, 0.33, 0.425, 0.53, 0.545, 0.475, 0.525, 0.43, 0.49, 0.47, 0.5, 0.355, 0.44, 0.365, 0.45, 0.355, 0.38, 0.565, 0.55, 0.615, 0.56, 0.58, 0.605, 0.575, 0.58, 0.665, 0.68, 0.465, 0.54, 0.45, 0.575, 0.355, 0.45, 0.24, 0.205, 0.21, 0.39, 0.47, 0.46, 0.325, 0.52, 0.4, 0.485, 0.47, 0.405, 0.5, 0.445, 0.47, 0.245, 0.505, 0.45, 0.505, 0.53, 0.52, 0.475, 0.475, 0.31, 0.555, 0.4, 0.595, 0.57, 0.605, 0.6, 0.595, 0.615, 0.575, 0.62, 0.52, 0.58, 0.57, 0.625, 0.56, 0.46, 0.575, 0.565, 0.555, 0.595, 0.625, 0.695, 0.665, 0.47, 0.4965, 0.651, 0.5125, 0.6925, 0.5995, 0.74, 0.702, 0.9585, 0.9575, 0.926, 0.873, 0.998, 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0.2215, 0.1955, 0.225, 0.22, 0.255, 0.289, 0.268, 0.335, 0.325, 0.348, 0.322, 0.3135, 0.271, 0.32, 0.288, 0.355, 0.38, 0.385, 0.4085, 0.43, 0.42, 0.461, 0.398, 0.4535, 0.449, 0.468, 0.0305, 0.038, 0.0505, 0.054, 0.056, 0.0585, 0.0625, 0.0775, 1.7025, 1.519, 1.454, 1.583, 1.5535, 1.74, 2.273, 1.219, 1.652, 2.0045, 1.6755, 1.9965, 1.187, 1.978, 2.124, 1.931, 2.236, 2.505, 2.0065, 2.657, 0.4645, 0.0765, 0.0685, 0.0835, 0.087, 0.08, 0.08, 0.1065, 0.098, 0.125, 0.12, 0.1205, 0.11, 0.142, 0.14, 0.15, 0.1645, 0.1535, 0.1855, 0.198, 0.2365, 0.1975, 0.2, 0.2425, 0.308, 0.277, 0.2365, 0.285, 0.1965, 0.271, 0.2575, 0.1125, 0.1075, 0.1055, 0.1375, 0.1145, 0.139, 0.2085, 0.1595, 0.2015, 0.179, 0.1945, 0.2005, 0.215, 0.1785, 0.196, 0.1875, 0.196, 0.213, 0.2445, 0.213, 0.2385, 0.218, 0.2465, 0.2025, 0.27, 0.2445, 0.279, 0.259, 0.294, 0.2425, 0.275, 0.36, 0.31, 0.239, 0.31, 0.29, 0.319, 0.374, 0.39, 0.302, 0.325, 0.369, 0.4115, 0.432, 0.3095, 0.405, 0.4, 0.335, 0.401, 0.325, 0.465, 0.445, 0.6195, 0.009, 0.1025, 0.1395, 0.148, 0.228, 0.215, 0.27, 0.365, 0.3045, 0.31, 0.371, 0.32, 0.315, 0.333, 0.335, 0.405, 0.3905, 0.48, 0.4305, 0.029, 0.035, 0.0365, 0.044, 0.035, 0.055, 0.078, 0.105, 0.125, 0.1, 0.13, 0.1455, 0.14, 0.125, 0.127, 0.135, 0.1355, 0.151, 0.195, 0.168, 0.215, 0.208, 0.215, 0.195, 0.19, 0.212, 0.205, 0.236, 0.24, 0.221, 0.247, 0.32, 0.2845, 0.265, 0.287, 0.2575, 0.3085, 0.279, 0.3555, 0.35, 0.2935, 0.317, 0.317, 0.332, 0.284, 0.305, 0.31, 0.425, 0.399, 0.3825, 0.4355, 0.385, 0.3885, 0.4, 0.398, 0.47, 0.358, 0.3315, 0.4165, 0.4, 0.341, 0.425, 0.4895, 0.46, 0.462, 0.44, 0.6745, 0.0465, 0.1255, 0.067, 0.1325, 0.273, 0.2475, 0.2455, 0.185, 0.255, 0.17, 0.146, 0.311, 0.4095, 0.271, 0.345, 0.283, 0.287, 0.355, 0.3235, 0.407, 0.4525, 0.034, 0.086, 0.123, 0.145, 0.11, 0.13, 0.1465, 0.165, 0.1945, 0.2595, 0.22, 0.1825, 0.148, 0.1895, 0.22, 0.2735, 0.275, 0.2695, 0.21, 0.235, 0.2985, 0.2445, 0.2875, 0.245, 0.314, 0.33, 0.2655, 0.3175, 0.335, 0.354, 0.315, 0.39, 0.37, 0.45, 0.456, 0.374, 0.512, 0.45, 0.5295, 0.44, 0.014, 0.05, 0.0355, 0.064, 0.0975, 0.1, 0.1325, 0.175, 0.17, 0.24, 0.0965, 0.1395, 0.153, 0.2245, 0.3305, 0.356, 0.296, 0.3945, 0.4575, 0.4285, 0.431, 0.275, 0.17, 0.085, 0.175, 0.105, 0.005, 0.055, 0.19, 0.245, 0.205, 0.32, 0.27, 0.355, 0.08, 0.159, 0.218, 0.256, 0.2105, 0.2615, 0.2175, 0.228, 0.3265, 0.284, 0.2985, 0.2675, 0.2645, 0.33, 0.328, 0.3335, 0.3055, 0.428, 0.3685, 0.438, 0.448, 0.4005, 0.4305, 0.372, 0.466, 0.3705, 0.4785, 0.346, 0.411, 0.48, 0.393, 0.5035, 0.441, 0.492, 0.5435, 0.4965, 0.5125, 0.553, 0.6765, 0.5795, 0.5695, 0.6215, 0.594, 0.551, 0.625, 0.6965, 0.5825, 0.663, 0.81, 0.5745, 0.5765, 0.7145, 0.8655, 0.7265, 0.8, 0.5945, 0.7895, 0.651, 0.8745, 0.7535, 0.7305, 0.8705, 0.7395, 0.7905, 0.8205, 0.688, 1.224, 0.974, 0.8735, 0.8495, 0.796, 0.744, 0.794, 0.7665, 0.8965, 0.857, 1.078, 0.892, 1.4395, 0.7955, 0.858, 0.9145, 1.171, 1.0505, 1.1135, 1.409, 1.065, 0.7995, 0.8855, 0.98, 1.026, 1.1315, 1.014, 1.12, 1.04, 0.8895, 0.9595, 1.238, 0.9755, 0.9025, 1.0835, 0.871, 1.0265, 0.9605, 1.137, 1, 1.004, 1.054, 1.023, 1.129, 1.2815, 1.2555, 1.293, 1.1295, 1.082, 1.15, 1.264, 1.2035, 1.3205, 1.272, 1.062, 1.101, 1.4595, 1.211, 1.1935, 0.986, 1.521, 1.3135, 1.1835, 1.401, 1.5075, 1.16, 1.5365, 1.567, 1.3595, 1.6445, 1.4545, 1.3585, 1.548, 1.337, 1.491, 1.0145, 1.7375, 1.7925, 1.7635, 2.21, 2.141, 1.5345, 1.614, 1.9335, 2.173, 1.737, 1.7375, 2.1275, 2.25, 2.5085, 2.255, 0.2215, 0.232, 0.3155, 0.3025, 0.329, 0.2815, 0.2825, 0.304, 0.2905, 0.389, 0.4405, 0.369, 0.4565, 0.4465, 0.436, 0.4615, 0.4315, 0.459, 0.566, 0.542, 0.4725, 0.6435, 0.573, 0.5525, 0.6855, 0.556, 0.9365, 0.559, 0.7205, 0.7045, 0.7575, 0.7405, 0.84, 0.793, 0.8745, 0.8715, 0.9875, 0.453, 0.1035, 0.285, 0.255, 0.255, 0.055, 0.49, 0.265, 0.19, 0.49, 0.185, 0.445, 0.05, 0.285, 0.195, 0.195, 0.205, 0.25, 0.17, 0.05, 0.365, 0.41, 0.155, 0.295, 0.3, 0.195, 0.11, 0.43, 0.185, 1.08, 1.165, 0.953, 0.906, 0.8725, 1.151, 0.9905, 1.04, 0.945, 0.9515, 1.201, 1.1005, 0.8425, 1.0725, 1.136, 1.1005, 1.1105, 1.1315, 1.271, 1.1345, 1.308, 1.5335, 1.546, 1.539, 1.5245, 1.3825, 1.175, 1.5555, 1.7835, 1.374, 1.389, 1.5265, 1.741, 1.7825, 1.684, 1.675, 2.1055, 2.21, 1.8725, 1.893, 0.101, 0.121, 0.1465, 0.1985, 0.2, 0.1845, 0.2175, 0.2165, 0.243, 0.237, 0.2365, 0.252, 0.28, 0.317, 0.303, 0.3295, 0.4195, 0.4, 0.4255, 0.4365, 0.6325, 0.4285, 0.464, 0.6025, 0.4615, 0.624, 0.4715, 0.602, 0.5955, 0.559, 0.6025, 0.6065, 0.4745, 0.6635, 0.5705, 0.646, 0.845, 0.615, 0.707, 0.6255, 0.555, 0.742, 0.8195, 0.972, 0.6275, 0.7205, 0.686, 0.686, 0.543, 0.717, 0.639, 0.7815, 0.7665, 0.765, 0.9005, 0.8225, 0.7235, 0.849, 0.743, 0.802, 0.831, 0.7805, 0.795, 0.833, 0.867, 0.7965, 1.116, 0.78, 0.985, 0.8015, 1.0305, 0.839, 0.9615, 0.834, 1.0335, 0.7095, 0.913, 1.207, 1.022, 0.9015, 0.9, 0.959, 0.8335, 1.13, 0.9165, 0.9175, 1.0645, 1.059, 1.194, 0.9775, 0.8025, 0.991, 0.974, 1.434, 0.983, 1.0725, 1.2405, 1.022, 1.1155, 1.1185, 1.224, 1.154, 1.139, 1.066, 1.2215, 0.924, 1.266, 1.0405, 1.1165, 1.0985, 1.1955, 1.362, 1.044, 1.1965, 1.1745, 1.233, 1.2695, 1.489, 1.4345, 1.2435, 1.2615, 0.355, 0.295, 0.165, 0.215, 0.39, 0.56, 0.345, 0.275, 0.005, 0.1085, 0.1775, 0.005, 0.35, 0.07, 0.02, 0.185, 0.145, 0.12, 0.205, 0.26, 0.225, 0.345, 0.275, 0.043, 0.08, 0.253, 0.09, 0.155, 0.655, 0.425, 0.3, 0.26, 0.21, 0.07, 0.25, 0.19, 0.355, 0.019, 0.19, 0.23, 0.27, 0.255, 0.184, 0.133, 0.1775, 0.263, 0.515, 0.4305, 0.496, 0.585, 0.0305, 0.03, 0.035, 0.045, 0.07, 0.066, 0.097, 0.1025, 0.119, 0.1405, 0.169, 0.155, 0.18, 0.225, 0.322, 0.34, 0.547, 0.5055, 0.4505, 0.01, 0.02, 0.3505, 0.062, 0.055, 0.083, 0.135, 0.17, 0.2015, 0.247, 0.2515, 0.305, 0.245, 0.323, 1.2235, 1.247, 1.707, 1.1605, 1.286, 1.5595, 1.4745, 1.33, 1.3375, 1.2215, 1.337, 1.488, 1.3835, 1.274, 1.043, 1.4405, 1.3195, 1.5175, 1.5505, 1.6375, 1.443, 1.289, 1.2915, 1.3725, 0.285, 0.225, 0.3, 0.335, 0.4295, 0.4715, 0.03, 0.0605, 0.0655, 0.0925, 0.109, 0.1075, 0.18, 0.172, 0.1805, 0.182, 0.1985, 0.19, 0.275, 0.236, 0.275, 0.239, 0.301, 0.269, 0.35, 0.34, 0.355, 0.3245, 0.4545, 0.499, 0.1415, 0.174, 0.278, 0.35, 0.305, 0.072, 0.0935, 0.0935, 0.085, 0.1185, 0.145, 0.1305, 0.19, 0.169, 0.216, 0.206, 0.225, 0.19, 0.2095, 0.3095, 0.3, 0.31, 0.3, 0.2755, 0.298, 0.27, 0.37, 0.3705, 0.37, 0.35, 0.37, 0.355, 0.381, 0.37, 0.386, 0.48, 0.506, 0.475, 0.5385, 0.48, 0.126, 0.139, 0.3795, 0.1785, 0.209, 0.2785, 0.3315, 0.3865, 0.042, 0.077, 0.15, 0.15, 0.165, 0.185, 0.252, 0.26, 0.24, 0.315, 0.335, 0.32, 0.335, 0.347, 0.424, 0.43, 0.365, 0.405, 0.657, 0.385, 0.355, 0.03, 0.046, 0.041, 0.11, 0.0885, 0.115, 0.146, 0.155, 0.14, 0.259, 0.3, 0.2845, 0.079, 0.081, 0.088, 0.156, 0.1765, 0.2405, 0.229, 0.249, 0.308, 0.296, 0.495), y = c(`1` = 15L, `2` = 7L, `3` = 9L, `4` = 10L, `5` = 7L, `6` = 8L, `7` = 20L, `8` = 16L, `9` = 9L, `11` = 14L, `12` = 10L, `13` = 11L, `15` = 10L, `16` = 12L, `17` = 7L, `18` = 10L, `19` = 7L, `20` = 9L, `21` = 11L, `22` = 10L, `23` = 12L, `24` = 9L, 1.2975, 1.2585, 1.39, 1.32, 1.619, 1.2835, 1.4535, 1.6075, 1.3735, 1.638, 1.569, 1.8565, 1.773, 2.009, 1.9805, 2.015, 2.125, 1.445, 2.1425, 1.9425, 2.119, 1.938, 2.5155, 2.7795, 0.2315, 0.597, 0.5965, 0.414, 0.422, 0.82, 0.434, 0.7615, 0.5795, 0.968, 0.869, 0.8795, 1.0615, 0.9225, 1.007, 0.65, 1.0835, 1.358, 1.3405, 1.025, 0.8145, 0.953, 0.8915, 1.038, 1.2525, 1.15, 1.0865, 1.169, 1.172, 1.1845, 1.0725, 1.3815, 1.6125, 1.4245, 1.3405, 1.4465, 1.679, 2.073, 1.5865, 2.069, 0.0555, 0.1275, 0.1885, 0.197, 0.2215, 0.2665, 0.269, 0.2805, 0.3305, 0.2815, 0.311, 0.368, 0.3405, 0.38, 0.3215, 0.358, 0.487, 0.4535, 0.568, 0.5155, 0.457, 0.5095, 0.494, 0.5605, 0.599, 0.7045, 0.7125, 0.6725, 0.5185, 0.6375, 0.5805, 0.6645, 0.581, 0.596, 0.6965, 0.6095, 0.677, 0.7245, 0.575, 0.632, 1.0015, 0.8145, 0.687, 0.7995, 0.599, 0.7075, 0.738, 0.837, 0.832, 0.8305, 0.8445, 1.0005, 0.88, 0.803, 0.785, 0.9765, 0.992, 1.013, 0.876, 0.8715, 0.8245, 0.7955, 0.857, 0.8265, 0.925, 1.0465, 0.7905, 0.886, 0.97, 1.1805, 0.99, 1.133, 1.071, 0.862, 1.1155, 1.031, 0.957, 1.0285, 1.0875, 1.255, 1.219, 1.195, 1.062, 1.5145, 1.0295, 1.0485, 1.1565, 1.09, 1.354, 0.984, 1.2525, 1.2695, 1.3405, 1.2895, 1.195, 1.273, 1.229, 1.1895, 1.3115, 1.1, 1.1285, 1.6195, 1.32, 1.5535, 1.238, 1.189, 1.375, 1.348, 1.1445, 1.4415, 1.218, 1.312, 1.473, 1.623, 1.534, 1.6915, 1.5715, 1.4405, 1.6245, 1.866, 1.6705, 1.8325, 2.173, 0.029, 0.0195, 0.0465, 0.1195, 0.0895, 0.0935, 0.1115, 0.1195, 0.097, 0.133, 0.1745, 0.1695, 0.17, 0.1605, 0.271, 0.196, 0.226, 0.22, 0.3, 0.2875, 0.3675, 0.346, 0.4955, 0.522, 0.441, 0.5185, 0.653, 0.5915, 0.6855, 0.741, 0.6355, 0.8395, 0.9205, 0.9255, 0.8295, 1.063, 1.115, 0.837, 0.982, 1.112, 1.3485, 1.5655, 0.086, 0.1065, 0.132, 0.1075, 0.1745, 0.152, 0.336, 0.355, 0.379, 0.378, 0.4655, 0.594, 0.4935, 0.4595, 0.4955, 0.4325, 0.689, 0.617, 0.7815, 0.6355, 0.6435, 0.738, 0.699, 0.7655, 0.7505, 0.844, 0.8705, 0.668, 0.83, 0.8685, 1.079, 0.919, 0.909, 1.2435, 1.056, 1.2095, 1.238, 1.644, 1.761, 1.914, 2.1, 1.6895, 2.032, 0.7455, 0.616, 0.2825, 0.3355, 0.651, 0.4775, 0.3465, 0.25, 0.66, 0.315, 0.276, 0.5395, 1.04, 1.041, 1.6885, 2.1835, 1.2105, 0.166, 0.452, 0.7185, 0.232, 0.0105, 0.0275, 0.1445, 0.282, 0.433, `25` = 10L, `26` = 11L, `27` = 11L, `29` = 15L, `30` = 11L, `31` = 10L, `33` = 18L, `34` = 19L, `36` = 8L, `37` = 16L, `38` = 8L, `39` = 11L, `40` = 9L, `41` = 9L, `43` = 5L, `44` = 5L, `45` = 4L, `46` = 7L, `47` = 9L, `48` = 7L, `49` = 6L, `51` = 8L, `52` = 7L, `53` = 10L, `54` = 10L, `55` = 7L, `56` = 8L, `57` = 8L, `58` = 8L, `59` = 4L, `60` = 7L, `61` = 7L, `62` = 9L, `63` = 10L, `65` = 8L, `66` = 8L, `69` = 10L, `70` = 6L, `71` = 13L, `72` = 8L, `73` = 20L, `74` = 11L, `75` = 13L, `76` = 15L, `77` = 9L, `80` = 14L, `81` = 9L, `82` = 12L, `83` = 16L, `85` = 14L, `86` = 12L, `87` = 13L, `88` = 10L, `89` = 9L, `90` = 12L, `91` = 15L, `92` = 12L, `93` = 13L, `94` = 10L, `95` = 15L, `96` = 14L, `98` = 8L, `99` = 7L, `100` = 10L, `101` = 7L, `102` = 15L, `106` = 12L, `107` = 11L, `108` = 10L, `111` = 9L, `112` = 9L, 0.3295, 0.4585, 0.275, 0.335, 0.4265, 0.127, 0.97, 0.4585, 0.4005, 0.7145, 0.932, 0.9995, 0.1735, 0.4665, 0.401, 0.3455, 0.5205, 0.3245, 0.106, 0.4415, 1.966, 0.1225, 0.879, 1.2275, 1.2715, 1.806, 1.3905, 1.8465, 0.897, 1.136, 0.2735, 0.2405, 0.0145, 0.1185, 0.0295, 0.0385, 0.799, 0.925, 1.098, 1.071, 0.975, 1.283, 1.0595, 0.9195, 0.9295, 0.8085, 0.6865, 0.6335, 0.585, 1.4935, 1.0405, 0.0605, 0.3675, 0.099, 0.233, 0.081, 1.4005, 0.9245, 1.36, 0.43, 0.14, 1.5765, 1.2125, 1.6305, 1.142, 1.539, 0.445, 0.19, 0.764, 0.853, 0.5885, 0.7875, 1.035, 1.352, 0.958, 0.797, 0.086, 0.729, 0.903, 1.0965, 0.903, 1.229, 1.106, 1.171, 0.444, 0.4755, 0.3785, 0.486, 0.2275, 0.3105, 0.543, 0.2225, 0.998, 1.2675, 1.1, 1.4285, 1.793, 0.6185, 0.5825, 0.7485, 1.1005, 0.869, 0.9215, 0.893, 1.0675, 0.8525, 0.8615, 1.162, 1.598, 1.259, 1.0935, 1.1805, 0.7175, 0.7575, 0.5705, 0.6905, 0.4475, 0.347, 0.7145, 0.3315, 0.18, 0.3215, 0.179, 0.702, 0.8045, 0.8075, 0.578, 0.385, 0.585, 0.6375, 0.5735, 0.94, 1.008, 0.775, 0.6, 0.515, 0.5795, `113` = 9L, `114` = 9L, `116` = 11L, `117` = 11L, `118` = 10L, `119` = 9L, `120` = 8L, `121` = 9L, `123` = 14L, `124` = 6L, `125` = 6L, `126` = 5L, `127` = 6L, `128` = 8L, `129` = 19L, `130` = 18L, `131` = 17L, `132` = 9L, `133` = 7L, `134` = 7L, `137` = 7L, `139` = 9L, `140` = 9L, `141` = 10L, `142` = 10L, `145` = 10L, `146` = 10L, `147` = 10L, `148` = 9L, `149` = 5L, `150` = 4L, `151` = 15L, `152` = 9L, `153` = 10L, `154` = 10L, `155` = 12L, `156` = 10L, `158` = 16L, `159` = 13L, `160` = 13L, `161` = 13L, `162` = 13L, `165` = 16L, `166` = 14L, `167` = 20L, `169` = 14L, `170` = 12L, `171` = 14L, `172` = 7L, `174` = 8L, `178` = 8L, `179` = 4L, `180` = 11L, `181` = 14L, `182` = 21L, `183` = 10L, `186` = 13L, `188` = 10L, `189` = 11L, `191` = 13L, 0.384, 0.2965, 0.137, 0.991, 0.925, 0.805, 0.8915, 0.6185, 0.6515, 0.2875, 0.7365, 0.306, 1.496, 0.3605, 0.3045, 0.592, 0.7995, 0.3435, 0.906, 2.526, 1.103, 0.957, 1.62, 1.407, 0.9, 0.1455, 1.247, 1.7265, 0.8025, 0.764, 0.8645, 0.4715, 0.631, 0.9455, 1.3185, 1.7265, 1.081, 0.9025, 1.027, 1.3445, 1.449, 0.7745, 0.7895, 1.0295, 1.031, 1.065, 1.885, 1.969, 0.9445, 0.354, 0.023, 0.3575, 0.5965, 0.383, 0.2065, 0.382, 0.6315, 0.3285, 0.2165, 0.0305, 0.4285, 0.496, 0.6195, 0.6505, 0.4495, 0.4485, 0.241, 1.2395, 1.0385, 0.972, 1.3315, 0.775, 0.437, 0.435, 0.1295, 1.25, 1.017, 1.411, 1.0705, 0.5895, 0.537, 0.15, 0.651, 0.287, 0.4765, 0.595, 0.4805, 0.321, 0.096, 0.9315, 0.0945, 0.213, 0.525, 1.312, 1.319, 0.8175, `193` = 14L, `194` = 8L, `195` = 10L, `196` = 12L, `198` = 16L, `201` = 9L, `202` = 13L, `204` = 13L, `205` = 8L, `206` = 9L, `208` = 8L, `209` = 13L, `211` = 10L, `212` = 7L, `216` = 10L, `217` = 8L, `218` = 7L, `219` = 10L, `220` = 8L, `221` = 9L, `222` = 11L, `223` = 9L, `224` = 11L, `226` = 9L, `227` = 7L, `228` = 7L, `229` = 11L, `230` = 15L, `232` = 14L, `233` = 22L, `234` = 7L, `235` = 12L, `236` = 9L, `237` = 1L, `238` = 3L, `240` = 5L, `242` = 5L, `243` = 5L, `244` = 8L, `245` = 8L, `246` = 10L, `247` = 13L, `248` = 9L, `249` = 7L, `251` = 7L, `252` = 13L, 0.17, 0.796, 0.4565, 0.528, 0.5835, 0.884, 0.096, 0.1855, 0.27, 0.463, 0.282, 0.0995, 0.0625, 0.073, 1.099, 0.5905, 0.768, 0.413, 0.8505, 0.6915, 0.709, 0.3755, 0.8185, 0.227, 0.8245, 1.1825, 1.122, 1.6115, 1.161, 1.489, 1.008, 0.441, 0.742, 0.7, 0.8565, 0.8975, 0.1925, 0.6635, 1.235, 0.7615, 0.592, 0.7565, 0.57, 0.2865, 0.2935, 0.567, 0.6245, 0.8415, 0.7775, 0.6905, 0.2, 0.109, 0.127, 0.2885, 0.18, 0.4665, 0.33, 0.34, 0.365, 0.6265, 0.5555, 0.53, 0.6315, 0.7715, 0.6125, 0.872, 0.8795, 0.983, 1.145, 1.262, 1.185, 1.332, 1.1405, 1.201, 1.362, 1.647, 1.4995, 1.791, 1.689, 1.659, 1.6015, 2.0165, 0.04, 0.0565, 0.064, 0.0875, 0.106, 0.1315, 0.126, 0.155, 0.279, 0.3205, 0.3335, 0.3885, 0.545, 0.4165, 0.495, 0.468, 0.4555, 0.4155, 0.4215, 0.474, 0.486, 0.5735, 0.468, 0.5135, 0.6215, 0.6005, 0.646, 0.7945, 0.812, 0.828, 0.681, 0.889, 0.7345, 0.964, 0.995, 0.948, 0.888, 0.9045, 1.046, 1.1775, 1.0765, 1.235, 1.247, 1.1495, 1.1575, 1.327, 1.2, 1.2705, 1.2155, 1.3245, 1.3555, 1.127, 1.4255, 1.286, 1.596, 1.297, 1.5105, 1.4415, 1.6385, 1.933, 1.8495, 1.742, 1.7975, 2.381, 2.1945, 0.049, 0.1055, 0.1045, 0.163, 0.2275, 0.219, 0.3265, 0.3465, 0.407, 0.4215, 0.3755, 0.5335, 0.442, 0.5535, 0.512, 0.583, 0.5765, 0.723, 0.5395, 0.625, 0.5925, `253` = 12L, `254` = 15L, `256` = 15L, `257` = 19L, `260` = 13L, `262` = 12L, `263` = 11L, `264` = 4L, `265` = 6L, `266` = 11L, `267` = 14L, `268` = 8L, `269` = 9L, `272` = 16L, `275` = 13L, `276` = 22L, `277` = 12L, `278` = 18L, `279` = 20L, `280` = 11L, `281` = 15L, `282` = 7L, `283` = 9L, `284` = 14L, `285` = 14L, `286` = 10L, `289` = 9L, `290` = 10L, `293` = 15L, `295` = 26L, `296` = 6L, `298` = 4L, `299` = 11L, `301` = 9L, `302` = 13L, `303` = 8L, `304` = 6L, `305` = 10L, `306` = 4L, `310` = 10L, `311` = 21L, `312` = 14L, `313` = 19L, `314` = 23L, `315` = 23L, `316` = 8L, `318` = 10L, `319` = 18L, `320` = 6L, `321` = 5L, `322` = 4L, `325` = 7L, `326` = 7L, `327` = 7L, `328` = 12L, `330` = 8L, `332` = 8L, `333` = 5L, `334` = 5L, `335` = 16L, 0.778, 0.7865, 0.778, 0.8435, 0.648, 0.784, 1.02, 0.811, 0.897, 0.852, 0.938, 0.6955, 0.861, 1.004, 1.0115, 1.1005, 1.108, 0.852, 1.9875, 1.079, 1.055, 1.198, 1.084, 1.1, 1.1015, `336` = 11L, `337` = 14L, `338` = 16L, `339` = 13L, `343` = 12L, `344` = 12L, `345` = 8L, `346` = 13L, `348` = 6L, `350` = 14L, `351` = 8L, `352` = 22L, `353` = 12L, `354` = 9L, `356` = 20L, `357` = 13L, `361` = 18L, `363` = 20L, `364` = 16L, `366` = 19L, `367` = 11L, `368` = 10L, `369` = 12L, `370` = 17L, `372` = 16L, `373` = 19L, `374` = 14L, `375` = 13L, `376` = 20L, `377` = 11L, `378` = 10L, `379` = 15L, `380` = 12L, `382` = 10L, `383` = 10L, `384` = 12L, `385` = 10L, `386` = 9L, `387` = 12L, `388` = 10L, `389` = 14L, `390` = 9L, `393` = 10L, `394` = 9L, `395` = 6L, 1.115, 1.3165, 1.3415, 1.027, 1.1845, 1.192, 1.285, 1.1805, 1.2655, 1.2895, 1.407, 1.4465, 1.349, 1.269, 1.68, 1.551, 1.4775, 1.529, 1.7585, 1.523, 1.7315, 1.51, 1.88, 2.012, 0.0315, 0.0985, 0.2015, 0.183, 0.187, 0.1795, 0.2065, 0.2295, 0.3075, 0.3415, 0.271, 0.91, 0.268, 0.2885, 0.3, 0.326, 0.314, 0.4025, 0.4305, 0.3505, 0.486, 0.471, 0.415, 0.5195, 0.5895, 0.5215, 0.5585, 0.596, 0.7665, 0.531, 0.607, 0.7055, 0.6445, 0.6315, 0.7845, 0.9475, 0.712, 0.89, 0.912, 0.656, 0.936, 0.6975, 1.1465, 0.825, 0.72, 0.8215, 1.017, 1.2255, 0.9865, `396` = 11L, `397` = 10L, `398` = 8L, `400` = 11L, `401` = 9L, `404` = 10L, `405` = 7L, `407` = 8L, `408` = 16L, `409` = 11L, `411` = 15L, `412` = 14L, `413` = 11L, `414` = 12L, `415` = 14L, `416` = 15L, `417` = 20L, `418` = 20L, `420` = 13L, `421` = 14L, `422` = 11L, `423` = 13L, `424` = 8L, `426` = 13L, `427` = 18L, `428` = 19L, `429` = 21L, `431` = 18L, `432` = 20L, `433` = 18L, `434` = 22L, `435` = 13L, `436` = 11L, `438` = 14L, `440` = 13L, `441` = 10L, `443` = 19L, `444` = 10L, `449` = 12L, `450` = 18L, `451` = 16L, `453` = 17L, `454` = 11L, `456` = 11L, `457` = 15L, 0.844, 0.885, 0.6735, 0.8755, 0.977, 1.0305, 1.05, 1.1145, 0.877, 1.3065, 0.8725, 1.0265, 1.1605, 1.0125, 1.096, 1.205, 1.221, 1.0005, 1.1355, 1.3135, 1.1955, 1.139, 1.368, 1.446, `458` = 9L, `459` = 10L, `460` = 11L, `461` = 11L, `463` = 6L, `464` = 5L, `465` = 6L, `467` = 12L, `468` = 13L, `469` = 17L, `470` = 21L, `471` = 9L, `472` = 10L, `473` = 9L, `475` = 10L, `476` = 17L, `477` = 9L, `478` = 17L, `479` = 21L, `480` = 16L, `481` = 29L, `482` = 17L, `483` = 15L, `484` = 19L, `485` = 12L, `486` = 13L, `488` = 15L, `489` = 11L, `490` = 14L, `491` = 14L, `492` = 13L, `494` = 15L, `495` = 17L, `496` = 15L, `497` = 12L, `499` = 11L, `501` = 12L, `504` = 13L, `505` = 17L, `506` = 15L, `507` = 12L, `508` = 15L, `509` = 11L, `510` = 16L, `511` = 10L, `512` = 10L, `513` = 10L, `514` = 6L, `516` = 6L, `517` = 9L, `518` = 5L, `519` = 4L, `521` = 3L, `523` = 9L, `524` = 5L, `525` = 4L, `527` = 3L, `528` = 14L, `531` = 20L, `532` = 13L, `533` = 12L, 1.4045, 1.463, 1.711, 1.757, 1.601, 2.165, 2.3305, 0.0545, 0.0725, 0.183, 0.1525, 0.187, 0.232, 0.2625, 0.4445, 0.511, 0.529, 0.872, 0.77, 0.8435, 0.818, 0.8035, 0.782, 0.8745, 0.984, 0.95, 1.1735, 0.976, 1.0785, 1.057, 1.11, 1.142, 1.1255, 1.1045, 1.3405, 1.2005, 1.243, 1.144, 1.309, 1.527, 1.6385, 1.691, 1.719, 1.52, 2.2355, 2.3235, 0.1245, 0.1285, 0.181, 0.1585, 0.2145, 0.3245, 0.2625, 0.371, 0.298, 0.336, 0.447, 0.4085, 0.587, 0.614, 0.5445, 0.6115, 0.5605, 0.521, 0.5775, 0.9505, 0.8315, 0.8725, 0.8155, 0.7595, 0.742, 0.804, 0.664, `535` = 10L, `536` = 11L, `537` = 11L, `538` = 7L, `540` = 10L, `541` = 15L, `542` = 11L, `543` = 15L, `544` = 8L, `545` = 10L, `546` = 11L, `547` = 8L, `550` = 11L, `551` = 17L, `552` = 13L, `553` = 12L, `554` = 13L, `555` = 9L, `556` = 10L, `557` = 14L, `558` = 12L, `560` = 13L, `561` = 8L, `563` = 11L, `564` = 11L, `565` = 14L, `566` = 10L, `568` = 12L, `570` = 11L, `571` = 16L, `572` = 14L, `573` = 20L, `574` = 17L, `575` = 10L, `576` = 11L, 0.745, 0.728, 0.777, 0.8765, 0.9895, 0.9065, 1.049, 0.92, 0.9525, 0.7855, 0.755, 0.9745, 1.2015, 0.9205, 1.056, 1.34, 1.0915, 0.9995, 1.0485, 1.2735, 1.0625, 1.1545, 1.194, 1.198, 0.9545, 1.3585, 1.2075, 1.217, 1.217, 0.8765, 1.2795, 1.167, 1.1885, 1.3765, 1.292, 1.1795, 1.297, 1.197, 1.539, 1.676, 1.6645, 1.528, 1.494, 1.5965, 1.3805, 1.527, 1.482, 1.2345, 1.825, 2.087, 1.884, 0.305, 0.4295, 0.449, 1.029, 0.981, `577` = 10L, `578` = 10L, `580` = 17L, `582` = 14L, `583` = 19L, `585` = 11L, `587` = 10L, `588` = 13L, `589` = 14L, `590` = 13L, `591` = 13L, `592` = 9L, `593` = 18L, `595` = 12L, `596` = 13L, `598` = 13L, `600` = 16L, `601` = 17L, `602` = 10L, `603` = 13L, `604` = 11L, `606` = 13L, `607` = 9L, `609` = 10L, `610` = 12L, `613` = 9L, `614` = 18L, `615` = 15L, `616` = 13L, `617` = 12L, `618` = 7L, `619` = 9L, `620` = 7L, `621` = 10L, `622` = 18L, `623` = 12L, `624` = 17L, `625` = 15L, `626` = 12L, `627` = 10L, `628` = 8L, `629` = 21L, `631` = 9L, `632` = 7L, `633` = 10L, `635` = 9L, `636` = 8L, `637` = 7L, `638` = 6L, `639` = 14L, `640` = 13L, `642` = 13L, `643` = 19L, `644` = 7L, `645` = 9L, `648` = 9L, `649` = 11L, `650` = 9L, `651` = 5L, `652` = 6L, 0.5745, 0.693, 1.183, 0.7905, 1.406, 0.873, 0.9415, 1.0835, 0.9845, 1.281, 1.148, 1.2235, 1.3695, 1.006, 1.307, 1.2905, 1.4265, 1.7885, 2.2305, 0.2075, 0.237, 0.215, 0.309, 0.3535, 0.335, 0.3875, 0.365, 0.4045, 0.372, 0.3965, 0.5225, 0.456, 0.44, 0.507, 0.5285, 0.572, 0.6625, 0.6445, 0.6275, 0.6885, 0.6835, 0.6855, 0.6755, 0.783, 0.768, 0.871, 0.7425, 0.737, 0.9175, 1.1275, 0.853, 0.991, 0.9125, 0.921, 0.8665, 1.089, 0.9615, 0.907, 1.1635, 1.1955, 1.31, 1.251, 1.154, 1.1335, 1.2015, 1.5595, 1.3165, 1.403, 1.204, 1.2895, 1.5175, 1.3575, `654` = 7L, `655` = 10L, `657` = 17L, `658` = 16L, `659` = 18L, `660` = 11L, `661` = 18L, `662` = 11L, `665` = 19L, `666` = 10L, `667` = 11L, `668` = 15L, `669` = 13L, `670` = 13L, `671` = 14L, `673` = 19L, `674` = 21L, `675` = 23L, `678` = 11L, `679` = 23L, `681` = 7L, `682` = 10L, `683` = 7L, `684` = 16L, `685` = 10L, `686` = 15L, `688` = 16L, `689` = 11L, `690` = 11L, `692` = 5L, `694` = 9L, `695` = 4L, `696` = 7L, `698` = 5L, `699` = 13L, `700` = 16L, `701` = 12L, `702` = 14L, `705` = 9L, `706` = 10L, `707` = 11L, `709` = 10L, `711` = 7L, `712` = 11L, `714` = 8L, `715` = 9L, `716` = 7L, `717` = 8L, `718` = 6L, `719` = 4L, `720` = 2L, `721` = 3L, `722` = 13L, `723` = 15L, `724` = 15L, `725` = 11L, `727` = 10L, `728` = 12L, `729` = 13L, `730` = 15L, `731` = 11L, `735` = 18L, `736` = 10L, `737` = 12L, `738` = 12L, `739` = 14L, `740` = 10L, `741` = 14L, `744` = 17L, `745` = 20L, `746` = 17L, `747` = 17L, `748` = 9L, `752` = 10L, `753` = 13L, `755` = 20L, `757` = 20L, `759` = 13L, `760` = 15L, `762` = 16L, `763` = 17L, `764` = 14L, `765` = 14L, `769` = 11L, `770` = 17L, `771` = 11L, 1.3645, 1.494, 1.664, 1.545, 1.5855, 1.908, 2.073, 0.0515, 0.057, 0.0925, 0.204, 0.3075, 0.262, 0.474, 0.5805, 0.728, 0.706, 0.5685, 0.655, 0.9115, 0.942, 1.193, 1.489, 0.1115, `772` = 8L, `774` = 9L, `775` = 10L, `776` = 11L, `777` = 15L, `778` = 13L, `779` = 15L, `780` = 15L, `782` = 12L, `783` = 10L, `785` = 5L, `786` = 15L, `787` = 9L, `788` = 12L, `789` = 9L, `790` = 12L, `791` = 10L, `792` = 10L, `794` = 12L, `795` = 12L, `796` = 15L, `797` = 11L, `798` = 9L, `799` = 11L, `800` = 11L, `801` = 10L, `802` = 10L, `803` = 9L, `804` = 7L, `805` = 10L, 0.1235, 0.2815, 0.2285, 0.2995, 0.361, 0.3045, 0.3975, 0.379, 0.7775, 0.751, 0.9215, 1.206, 1.2865, 1.1945, 1.1655, 1.1735, 1.532, 1.5105, 0.5565, 0.547, 0.5165, 0.764, 0.488, 0.6905, 0.1835, 0.97, 0.033, 0.059, 1.3235, 0.8465, 1.047, 1.2785, 1.367, 1.742, 0.513, 0.56, 0.635, 0.397, 0.806, 0.1165, 0.17, 1.194, 0.765, 1.151, 0.6765, 1.153, 0.9125, 0.872, 0.792, 0.3955, 0.657, 0.2505, 0.102, 0.551, 0.9185, 0.4965, 0.632, 2.2695, 0.0345, 1.3315, 0.72, 1.032, 0.1495, 0.1515, 0.478, 0.578, 0.6835, 0.2725, 1.511, 1.3035, 1.3955, 0.116, 0.523, 1.0055, 1.1345, 1.0835, 0.868, 1.0915, 1.271, 0.388, 1.2355, 0.6025, 0.973, 0.753, 0.375, 0.4135, 0.56, 0.3915, 1.2765, 0.96, 1.2955, 1.383, 0.2225, 1.156, 1.477, 1.5715, 0.858, 1.4995, 1.467, 1.092, 1.8165, 1.6365, 0.906, 1.213, 1.06, 0.161, 0.459, 0.533, 0.6175, 0.366, 0.4195, 0.15, 0.8745, 0.655, 0.484, 1.4435, 0.8775, 1.3105, 0.9085, 1.1705, 0.528, 0.6625, 0.4335, 0.3595, 1.107, 0.8595, 0.4365, 0.501, 0.6735, 0.415, 0.4775, 0.5555, 1.428, 1.6195, 1.0385, 0.7085, 0.72, 0.6175, 0.738, 1.22, 0.7485, 1.0735, 1.145, 1.1055, 1.67, `808` = 15L, `810` = 12L, `811` = 12L, `813` = 6L, `815` = 7L, `816` = 7L, `817` = 6L, `818` = 6L, `819` = 6L, `821` = 7L, `822` = 6L, `823` = 7L, `824` = 6L, `827` = 6L, `829` = 6L, `830` = 7L, `831` = 6L, 1.555, 0.8085, 1.057, 0.438, 0.099, 0.5085, 1.166, 0.507, 0.6345, 0.314, 0.4385, 0.195, 0.084, 1.4685, 0.7765, 0.5425, 0.3375, 0.2565, 0.163, 1.3655, 0.967, 0.6855, 0.314, 0.7005, `832` = 6L, `833` = 9L, `834` = 7L, `835` = 7L, `837` = 8L, `839` = 8L, `840` = 9L, `841` = 8L, `842` = 9L, `843` = 9L, `845` = 8L, `846` = 9L, `847` = 11L, `848` = 10L, `850` = 10L, `851` = 10L, `855` = 10L, `856` = 9L, `857` = 9L, `860` = 8L, `862` = 12L, `863` = 11L, `864` = 10L, `865` = 11L, `867` = 9L, `868` = 12L, `869` = 10L, `870` = 9L, `871` = 12L, `872` = 11L, `873` = 9L, `874` = 11L, `875` = 11L, `876` = 14L, `877` = 9L, `879` = 9L, `880` = 8L, `881` = 11L, `882` = 9L, `883` = 17L, `884` = 15L, `886` = 10L, `887` = 12L, `888` = 9L, `889` = 11L, `890` = 11L, `891` = 11L, `892` = 17L, `894` = 5L, `895` = 3L, `896` = 6L, `897` = 4L, `899` = 4L, `900` = 5L, `901` = 4L, `903` = 5L, `905` = 8L, `906` = 5L, `909` = 7L, `910` = 7L, `911` = 5L, `912` = 7L, `913` = 7L, `914` = 6L, `918` = 7L, `920` = 6L, `922` = 5L, `923` = 6L, `924` = 6L, `925` = 6L, `927` = 7L, `928` = 8L, `929` = 6L, `930` = 6L, `932` = 6L, `933` = 7L, `934` = 7L, `935` = 8L, `936` = 7L, `937` = 7L, `939` = 6L, `940` = 7L, `942` = 7L, `945` = 6L, `947` = 8L, `948` = 9L, `950` = 6L, `951` = 6L, `952` = 8L, `953` = 7L, `954` = 7L, `955` = 8L, `956` = 8L, `957` = 7L, `958` = 6L, `959` = 8L, `960` = 7L, `961` = 7L, `962` = 9L, `963` = 8L, `964` = 9L, `965` = 8L, `966` = 8L, `967` = 7L, `969` = 8L, `970` = 8L, `972` = 7L, `973` = 8L, `975` = 8L, `976` = 8L, `978` = 8L, `979` = 8L, `981` = 9L, `983` = 9L, `984` = 8L, `986` = 10L, `987` = 8L, `988` = 8L, `989` = 7L, `990` = 9L, `994` = 10L, `995` = 8L, `998` = 8L, `999` = 8L, `1000` = 11L, `1001` = 11L, `1002` = 9L, `1003` = 9L, `1005` = 9L, `1008` = 11L, `1009` = 10L, `1010` = 8L, `1011` = 11L, `1012` = 10L, `1013` = 10L, `1014` = 10L, `1015` = 9L, 0.4895, 0.45, 0.382, 0.4435, 1.6935, 1.71, 0.8575, 0.97, 0.59, 0.34, 0.385, 0.515, 0.59, 0.3005, 0.683, 0.468, 0.4075, 0.4545, 0.4385, 0.407, 0.106, 1.073, 0.301, 0.7585, 0.171, 0.3625, 1.0885, 1.278, 0.44, 0.163, 0.9715, 0.5325, 0.127, 0.1635, 0.1, 0.9955, 0.6605, 0.721, 0.487, 0.955, 0.873, 1.65, 1.1285, 1.1115, 0.9745, 1.4605, 1.0105, 1.0765, 1.616, 0.2205, 0.44, 0.6815, 0.5795, 0.209, 0.224, 0.1765, 0.196, 0.192, 0.3095, 0.313, 0.3595, 0.674, 0.5265, 1.0315, 1.0165, 1.2495, 1.1685, 1.148, 1.089, 1.9045, 2.4925, 0.0805, 0.111, `1016` = 9L, `1017` = 8L, `1018` = 11L, `1019` = 8L, `1020` = 11L, `1021` = 10L, `1023` = 11L, `1025` = 10L, `1026` = 11L, `1027` = 8L, `1029` = 11L, `1030` = 10L, `1031` = 10L, `1032` = 9L, `1033` = 11L, `1034` = 10L, `1037` = 11L, `1038` = 9L, `1039` = 10L, `1040` = 6L, `1041` = 11L, `1042` = 10L, `1043` = 12L, `1044` = 12L, `1045` = 8L, `1047` = 10L, `1048` = 12L, `1049` = 11L, `1050` = 10L, `1051` = 11L, `1052` = 11L, `1053` = 12L, `1056` = 4L, `1057` = 4L, `1058` = 4L, `1059` = 4L, `1060` = 5L, `1061` = 7L, `1062` = 6L, `1064` = 5L, `1065` = 6L, `1066` = 6L, `1067` = 6L, `1068` = 5L, `1069` = 6L, 0.152, 0.2155, 0.2565, 0.2505, 0.279, 0.378, 0.456, 0.5055, 0.4705, 0.643, 0.513, 0.596, 0.5725, 0.6435, 0.8115, 0.7115, 0.9155, 0.8815, 0.931, 1.065, 1.1385, 1.234, 1.0745, 1.403, `1071` = 6L, `1072` = 7L, `1073` = 6L, `1074` = 7L, `1076` = 6L, `1077` = 7L, `1079` = 6L, `1080` = 7L, `1081` = 7L, `1083` = 7L, `1084` = 7L, `1086` = 7L, `1089` = 6L, `1091` = 6L, `1093` = 7L, 1.1325, 1.1115, 1.437, 1.3635, 1.1435, 1.478, 1.6885, 2.0725, 1.523, 0.2675, 0.258, 0.2985, 0.411, 0.4015, 0.4485, 0.4155, 0.7095, 0.637, 1.1155, 0.5345, 0.985, 0.85, 0.838, 0.812, `1094` = 7L, `1095` = 6L, `1096` = 6L, `1097` = 7L, `1098` = 8L, `1099` = 7L, `1101` = 9L, `1102` = 8L, `1103` = 6L, `1104` = 7L, `1105` = 8L, `1106` = 6L, `1107` = 9L, `1108` = 8L, `1109` = 8L, `1110` = 9L, `1112` = 8L, `1113` = 7L, `1115` = 9L, `1117` = 8L, `1118` = 8L, `1120` = 10L, `1121` = 7L, `1122` = 9L, `1123` = 9L, `1124` = 8L, `1125` = 8L, `1126` = 10L, `1127` = 9L, `1128` = 8L, `1129` = 8L, `1130` = 8L, `1131` = 9L, `1133` = 9L, `1135` = 8L, `1138` = 10L, `1140` = 9L, `1141` = 9L, `1143` = 9L, `1144` = 9L, `1145` = 8L, `1146` = 9L, `1147` = 9L, `1148` = 9L, `1153` = 8L, `1154` = 9L, `1155` = 8L, `1156` = 8L, `1159` = 9L, `1160` = 10L, `1161` = 9L, `1162` = 8L, `1163` = 9L, `1164` = 9L, `1165` = 10L, `1166` = 9L, `1167` = 9L, `1168` = 8L, `1169` = 8L, `1170` = 8L, `1171` = 9L, `1172` = 8L, `1173` = 8L, `1174` = 10L, `1175` = 9L, `1176` = 10L, `1177` = 10L, `1178` = 10L, `1179` = 9L, `1180` = 11L, `1181` = 11L, `1182` = 10L, `1184` = 8L, `1185` = 9L, `1186` = 9L, `1187` = 14L, `1188` = 10L, `1189` = 9L, `1190` = 12L, `1191` = 9L, `1192` = 10L, `1194` = 12L, `1196` = 9L, `1197` = 11L, `1198` = 12L, `1199` = 10L, `1201` = 10L, `1202` = 8L, `1203` = 12L, `1205` = 11L, 0.7615, 0.87, 0.9835, 0.929, 1.0155, 0.939, 1.164, 1.214, 1.1345, 1.233, 1.0815, 1.179, 1.1635, 1.1545, 1.3345, 1.469, 1.5585, 1.3275, 1.6615, 1.492, 1.7365, 1.6605, 1.348, 1.7405, `1206` = 11L, `1207` = 11L, `1208` = 11L, `1209` = 10L, `1210` = 11L, `1211` = 6L, `1212` = 4L, `1213` = 5L, `1214` = 5L, `1215` = 6L, `1216` = 5L, `1217` = 5L, `1218` = 5L, `1219` = 5L, `1220` = 6L, `1221` = 7L, `1222` = 5L, `1223` = 7L, `1224` = 6L, `1225` = 6L, `1226` = 5L, `1227` = 6L, `1228` = 8L, `1229` = 4L, `1230` = 6L, `1231` = 7L, `1232` = 7L, `1233` = 7L, `1235` = 5L, `1236` = 7L, 1.7185, 0.097, 0.131, 0.1695, 0.192, 0.1735, 0.2055, 0.2345, 0.2305, 0.314, 0.33, 0.2775, 0.405, 0.309, 0.396, 0.3615, 0.4265, 0.384, 0.4865, 0.498, 0.494, 0.528, 0.5585, 0.636, 0.658, 0.78, 0.6075, 0.7025, 0.8725, 1.2365, 1.186, 0.7745, 0.8725, 0.8515, 0.895, 0.886, 0.9265, 0.907, 1.041, 0.9355, 1.058, 0.7955, 0.928, 1.157, 1.088, 0.776, 1.043, 1.1415, 1.3335, 1.273, 1.1835, 1.2135, 1.0315, 1.3745, 1.463, 1.6275, 1.3875, 1.46, 1.2835, 1.3765, 1.6465, 1.265, 1.56, 1.6, 2.333, 0.028, 0.325, 0.5755, 0.5805, 0.8035, 0.8675, 1.0425, 1.14, 1.3475, 1.0735, 1.304, 1.12, 1.2645, 1.275, 1.27, 1.583, 1.42, 1.5955, 1.89, 0.084, 0.1005, 0.1285, 0.1345, 0.1195, 0.1895, 0.2745, 0.342, 0.344, 0.3275, 0.4035, 0.4075, 0.4975, 0.3685, 0.4175, 0.4425, 0.4225, 0.426, 0.573, 0.657, 0.781, 0.6765, 0.769, 0.6235, 0.7315, 0.737, 0.654, 0.7915, 0.753, 0.717, 0.783, 1.034, 0.9855, 0.7635, 0.9365, 0.964, 1.0455, `1237` = 6L, `1238` = 6L, `1239` = 8L, `1240` = 7L, `1241` = 6L, `1242` = 8L, `1243` = 9L, `1244` = 8L, `1246` = 5L, `1247` = 7L, `1248` = 8L, `1250` = 5L, `1251` = 5L, `1252` = 6L, `1253` = 7L, `1254` = 6L, `1255` = 7L, `1256` = 7L, `1258` = 8L, `1260` = 7L, `1261` = 9L, `1262` = 9L, `1263` = 8L, `1264` = 8L, `1265` = 8L, `1266` = 7L, `1267` = 9L, `1268` = 8L, `1269` = 11L, `1270` = 8L, `1271` = 8L, `1272` = 8L, `1273` = 8L, `1274` = 8L, `1275` = 8L, `1276` = 9L, `1279` = 8L, `1280` = 7L, `1281` = 8L, `1283` = 9L, `1284` = 9L, `1286` = 7L, `1287` = 9L, `1289` = 7L, `1290` = 8L, `1291` = 7L, `1293` = 9L, `1294` = 10L, `1295` = 9L, `1299` = 10L, `1300` = 9L, `1303` = 8L, `1304` = 9L, `1306` = 10L, `1307` = 9L, 1.081, 1.1245, 1.1345, 1.0805, 1.0835, 1.242, 1.3915, 0.966, 1.0855, 1.217, 1.4865, 1.276, 1.281, 1.4275, 1.155, 1.35, 1.3685, 1.431, 1.764, 1.278, 1.217, 1.46, 1.5575, 1.527, `1309` = 9L, `1311` = 8L, `1312` = 11L, `1313` = 9L, `1314` = 9L, `1315` = 10L, `1316` = 9L, `1317` = 9L, `1318` = 10L, `1320` = 9L, `1321` = 9L, `1322` = 9L, `1323` = 10L, `1324` = 9L, `1325` = 11L, `1326` = 8L, `1327` = 8L, `1328` = 11L, `1330` = 8L, `1333` = 10L, `1334` = 9L, `1335` = 10L, `1336` = 9L, `1337` = 10L, `1338` = 10L, `1339` = 10L, `1340` = 8L, `1341` = 9L, `1342` = 10L, `1343` = 10L, `1344` = 10L, `1345` = 10L, `1346` = 10L, `1347` = 8L, `1350` = 10L, `1351` = 10L, `1352` = 12L, `1353` = 10L, `1354` = 11L, `1355` = 10L, `1356` = 11L, `1357` = 10L, `1358` = 11L, `1359` = 11L, `1360` = 10L, `1361` = 11L, `1363` = 12L, `1364` = 10L, `1366` = 10L, `1367` = 9L, 1.4985, 1.885, 1.432, 1.4385, 1.564, 2.548, 0.1585, 0.4015, 0.2885, 0.4915, 0.9145, 0.976, 0.7865, 0.775, 0.8415, 0.681, 0.6145, 1.1655, 1.764, 1.1215, 1.167, 1.0375, 1.124, 1.2635, 1.4045, 1.37, 1.8185, 0.101, 0.2575, 0.32, 0.4155, 0.3935, 0.452, 0.5065, 0.5525, 0.699, 0.7905, 0.702, 0.675, 0.475, 0.612, 0.705, 0.846, 0.86, 0.8875, 0.6545, 1.0535, 0.927, 0.8275, 0.9845, 0.86, 1.1785, 1.2205, 0.9685, 1.2555, 1.2425, 1.09, 1.072, 1.2075, 1.227, 1.472, 1.5785, 1.4755, 1.5545, 1.593, 1.8995, 1.4575, 0.0495, 0.1455, 0.129, 0.2255, 0.359, 0.3505, 0.5885, 0.623, 0.5375, 0.8625, 0.386, 0.5315, 0.562, `1368` = 10L, `1369` = 10L, `1370` = 10L, `1372` = 10L, `1373` = 12L, `1374` = 10L, `1377` = 10L, `1378` = 12L, `1379` = 10L, `1380` = 10L, `1381` = 9L, `1382` = 11L, `1383` = 9L, `1384` = 12L, `1385` = 9L, `1386` = 11L, `1387` = 12L, `1390` = 10L, `1391` = 9L, `1392` = 9L, `1393` = 10L, `1394` = 10L, `1395` = 15L, `1396` = 10L, `1397` = 11L, `1398` = 10L, `1399` = 11L, `1400` = 11L, `1402` = 11L, `1404` = 10L, `1405` = 10L, `1406` = 13L, `1407` = 10L, `1408` = 10L, `1409` = 10L, `1410` = 10L, `1411` = 10L, `1413` = 10L, `1414` = 13L, `1415` = 9L, `1416` = 11L, `1418` = 10L, `1419` = 11L, `1420` = 12L, `1421` = 11L, `1422` = 13L, `1423` = 12L, `1424` = 11L, `1425` = 11L, `1426` = 12L, 0.836, 1.03, 1.1685, 1.127, 1.1665, 1.541, 1.7825, 1.47, 0.754, 0.4485, 0.22, 0.4425, 0.3555, 0.034, 0.185, 0.79, 0.8255, 0.62, 0.9525, 0.955, 0.91, 0.2045, 0.515, 1.604, 0.3005, 0.8255, 0.9685, 0.731, 0.1785, 1.373, 1.018, 1.082, 1.423, 0.655, 1.4155, 0.1385, 0.782, 0.717, 0.6355, 0.656, 0.744, 0.578, 0.1405, 0.8615, 1.626, 0.478, 1.1775, 1.058, `1427` = 9L, `1428` = 14L, `1429` = 14L, `1432` = 7L, `1434` = 6L, `1435` = 7L, `1437` = 5L, `1438` = 6L, `1439` = 7L, `1441` = 7L, `1442` = 6L, `1443` = 5L, `1445` = 6L, `1446` = 6L, `1447` = 5L, `1448` = 8L, `1449` = 9L, `1452` = 8L, `1453` = 9L, `1454` = 6L, `1457` = 7L, `1458` = 7L, `1459` = 7L, `1460` = 6L, `1461` = 8L, `1463` = 7L, `1464` = 9L, `1465` = 9L, `1466` = 8L, `1467` = 8L, `1469` = 8L, `1470` = 8L, `1471` = 9L, `1472` = 8L, `1473` = 9L, `1474` = 8L, `1475` = 9L, `1476` = 8L, `1477` = 9L, `1478` = 8L, `1479` = 9L, `1481` = 9L, `1482` = 9L, `1483` = 8L, `1484` = 8L, `1485` = 9L, `1486` = 8L, `1487` = 9L, `1488` = 8L, `1490` = 11L, `1491` = 9L, `1493` = 11L, `1494` = 8L, `1497` = 9L, `1499` = 11L, `1500` = 9L, `1501` = 10L, `1502` = 9L, `1503` = 11L, `1505` = 10L, `1506` = 8L, `1507` = 9L, `1508` = 10L, `1509` = 11L, `1511` = 10L, `1513` = 9L, `1514` = 8L, `1515` = 11L, `1516` = 11L, `1517` = 10L, `1518` = 10L, `1520` = 10L, `1522` = 12L, `1524` = 12L, `1525` = 11L, `1527` = 10L, `1528` = 12L, `1529` = 13L, `1530` = 10L, `1531` = 11L, `1533` = 5L, `1534` = 6L, `1536` = 6L, `1540` = 7L, `1541` = 8L, `1543` = 7L, `1545` = 6L, `1546` = 7L, `1547` = 7L, `1548` = 6L, `1549` = 8L, `1552` = 7L, `1556` = 8L, `1558` = 8L, `1559` = 7L, `1560` = 7L, `1563` = 7L, `1565` = 7L, `1566` = 7L, `1567` = 9L, `1570` = 8L, `1571` = 7L, `1575` = 8L, `1576` = 7L, `1577` = 8L, `1578` = 8L, `1579` = 8L, `1580` = 8L, `1582` = 8L, `1583` = 9L, `1584` = 7L, `1585` = 6L, `1587` = 10L, `1588` = 8L, `1589` = 8L, `1590` = 9L, `1592` = 8L, `1594` = 8L, `1596` = 9L, `1598` = 10L, `1599` = 9L, `1603` = 9L, `1604` = 9L, `1605` = 10L, `1606` = 9L, `1607` = 10L, `1608` = 8L, `1609` = 9L, `1610` = 8L, `1611` = 8L, `1613` = 9L, `1615` = 8L, `1616` = 8L, `1618` = 8L, `1619` = 11L, 0.817, 0.335, 1.221, 0.7365, 0.97, 0.8085, 0.4075, 0.6295, 1.053, 1.829, 1.1765, 0.8765, 0.0145, 0.476, 0.813, 0.018, 0.853, 0.219, 0.0635, 0.6275, 0.441, 0.458, 0.6765, 0.7325, 0.712, 1.3065, 0.8545, 0.125, 0.2225, 0.6955, 0.266, 0.487, 2.0495, 1.498, 0.8605, 0.77, 0.541, 0.205, 0.8695, 0.6555, 1.0905, 0.0615, 0.6175, 0.772, 0.7155, 0.717, 0.5805, 0.556, 0.591, 1.016, 1.807, 1.777, 2.173, 2.1505, 0.118, 0.099, 0.1365, 0.164, 0.248, 0.252, 0.4015, 0.3905, 0.4095, 0.494, 0.599, 0.6245, 0.7745, 0.874, 1.21, 1.37, 1.759, 1.8065, 1.5945, 0.032, 0.0785, 0.134, 0.2075, 0.2235, 0.34, 0.635, 0.5815, 0.7545, 0.98, 1.0035, 1.133, 1.142, 1.131, 1.065, 1.181, 1.2275, 1.4415, 1.473, 1.658, 0.096, 0.196, 0.2155, 0.2805, 0.366, 0.4965, 0.651, 0.5125, 0.6925, 0.5995, 0.74, 0.702, 0.9585, 0.9575, 0.926, 0.873, 0.998, 0.6655, 1.234, 1.025, 1.2935, 1.7855, 1.403, 1.826, 0.6125, 0.658, 0.911, 1.336, 1.36, 0.2325, 0.317, 0.318, 0.2645, 0.4055, 0.524, 0.439, 0.5805, 0.622, 0.7805, 0.741, 0.8265, 0.7275, 0.7005, `1620` = 10L, `1621` = 8L, `1622` = 10L, `1623` = 9L, `1625` = 9L, `1626` = 10L, `1627` = 8L, `1628` = 9L, `1630` = 10L, `1631` = 9L, `1634` = 10L, `1635` = 10L, `1636` = 9L, `1637` = 10L, `1639` = 9L, `1641` = 8L, `1642` = 9L, `1643` = 13L, `1644` = 8L, `1645` = 10L, `1647` = 9L, `1648` = 10L, `1649` = 10L, `1650` = 10L, `1651` = 11L, `1652` = 8L, `1654` = 10L, `1655` = 8L, `1657` = 9L, `1658` = 9L, `1659` = 8L, `1660` = 10L, `1661` = 9L, `1662` = 10L, `1663` = 9L, `1665` = 9L, `1666` = 8L, `1667` = 9L, `1668` = 11L, `1669` = 10L, `1670` = 10L, `1671` = 12L, `1672` = 9L, `1673` = 10L, `1675` = 9L, `1678` = 9L, `1679` = 12L, `1682` = 11L, `1683` = 11L, `1684` = 12L, 1.1815, 1.0145, 1.1, 1.218, 0.984, 1.0105, 1.0765, 1.337, 1.3705, 1.2435, 1.073, 1.2915, 1.3105, 1.4915, 1.494, 1.1965, 1.672, 1.9125, 1.907, 1.79, 1.7495, 0.519, 0.5305, 1.2825, 0.7165, 0.9645, 1.2415, 1.3185, 1.3455, 0.1185, 0.197, 0.4725, 0.4495, 0.516, 0.709, 0.7385, 0.7935, 0.7655, 0.985, 1.13, 1.0805, 1.229, 1.2355, 1.2395, 1.357, 1.402, 1.359, 1.7405, `1686` = 10L, `1687` = 12L, `1688` = 9L, `1689` = 11L, `1691` = 9L, `1692` = 12L, `1693` = 10L, `1694` = 9L, `1696` = 8L, `1697` = 11L, `1698` = 10L, `1699` = 11L, `1700` = 13L, `1701` = 12L, `1702` = 10L, `1703` = 9L, `1704` = 10L, `1705` = 10L, `1706` = 11L, `1707` = 10L, `1708` = 12L, `1709` = 14L, `1710` = 9L, `1712` = 9L, `1714` = 10L, `1716` = 9L, `1718` = 9L, `1720` = 11L, `1721` = 9L, `1722` = 10L, 1.51, 2.0505, 0.124, 0.14, 0.1695, 0.384, 0.393, 0.3805, 0.4895, 0.5135, 0.4495, 0.8025, 1.053, 0.9945, 0.344, 0.2845, 0.3035, 0.499, 0.615, 0.8395, 0.8675, 0.887, 1.176, 1.0945, 1.9485), ShuckedWeight = c(0.2245, 0.0995, 0.2565, 0.2155, 0.0895, 0.141, 0.237, 0.294, 0.2165, 0.194, 0.1675, 0.2175, 0.1675, 0.258, 0.095, 0.188, 0.097, 0.1705, 0.0955, 0.08, 0.4275, 0.318, 0.513, 0.3825, 0.3945, 0.394, 0.393, 0.3935, 0.5515, 0.815, 0.227, 0.5305, 0.237, 0.381, 0.134, 0.1865, 0.0315, 0.0255, 0.0175, 0.0875, 0.293, 0.1775, 0.0755, 0.2385, 0.1335, 0.2595, 0.2105, 0.173, 0.2565, 0.192, 0.2765, 0.042, 0.246, 0.18, 0.305, 0.302, 0.234, 0.234, 0.2135, 0.063, 0.264, 0.1405, 0.48, 0.474, 0.481, 0.4425, 0.3625, 0.4695, 0.3845, 0.5105, 0.396, 0.38, 0.339, 0.4825, 0.3305, 0.2205, 0.3135, 0.341, 0.307, 0.4015, 0.507, 0.588, 0.5755, 0.214, 0.201, 0.2775, 0.105, 0.328, 0.291, 0.2935, 0.261, 0.2375, 0.172, `1723` = 9L, `1725` = 11L, `1726` = 9L, `1727` = 12L, `1728` = 10L, `1729` = 14L, `1730` = 11L, `1731` = 11L, `1732` = 11L, `1733` = 12L, `1734` = 9L, `1735` = 12L, `1736` = 11L, `1737` = 13L, `1739` = 11L, `1740` = 13L, `1742` = 10L, `1743` = 11L, `1744` = 11L, `1745` = 11L, `1746` = 12L, `1747` = 10L, `1748` = 15L, `1750` = 11L, `1751` = 12L, `1753` = 11L, `1755` = 13L, `1756` = 7L, `1757` = 14L, `1758` = 11L, `1760` = 12L, `1761` = 11L, `1763` = 12L, `1764` = 12L, `1765` = 5L, `1767` = 7L, `1769` = 8L, `1770` = 7L, `1771` = 6L, `1772` = 8L, `1773` = 8L, `1774` = 10L, `1775` = 7L, `1777` = 9L, `1778` = 9L, 0.1485, 0.13, 0.233, 0.2555, 0.1305, 0.3465, 0.1145, 0.163, 0.304, 0.09, 0.081, 0.0285, 0.1075, 0.112, 0.63, 0.7665, 0.4835, 0.175, 0.085, 0.086, 0.0675, 0.151, 0.096, 0.341, 0.295, 0.2075, 0.245, 0.224, 0.052, 0.0105, 0.013, 0.4415, 0.469, 0.5065, 0.37, 0.387, 0.32, 0.546, 0.374, 0.2985, 0.4215, 0.53, 0.5605, 1.0705, 0.765, 0.8175, 0.5125, 0.7125, 0.308, 0.32, 0.053, 0.025, 0.385, 0.399, 0.515, 0.429, 0.5455, 0.54, 0.5265, 0.4845, 0.276, 0.092, 0.459, 0.346, 0.4445, 0.1655, 0.254, 0.2985, 0.171, 0.186, 0.1495, 0.2755, 0.1995, 0.0755, 0.244, 0.174, 0.1265, 0.1665, 0.1305, 0.1785, 0.187, 0.22, 0.181, 0.1715, 0.121, 0.078, 0.404, 0.2935, 0.31, 0.496, 0.0485, 0.1815, 0.0485, 0.001, 0.0045, 0.0075, 0.034, 0.026, 0.0435, 0.063, 0.0705, 0.0635, 0.0745, 0.0675, 0.0655, 0.4205, 0.5135, 0.4485, 0.3835, 0.3805, 0.4775, 0.3675, 0.319, 0.0445, 0.0465, 0.3, 0.3615, 0.1225, 0.2185, 0.412, 0.4105, 0.7425, 0.493, 0.532, 0.55, 0.2665, 0.314, 0.0915, 0.177, 0.2285, 0.325, 0.295, 0.132, 0.3155, 0.428, 0.384, 0.049, 0.039, `1779` = 8L, `1780` = 9L, `1781` = 9L, `1782` = 8L, `1784` = 7L, `1787` = 8L, `1789` = 10L, `1792` = 10L, `1794` = 10L, `1795` = 10L, `1797` = 10L, `1798` = 8L, `1800` = 9L, `1801` = 9L, `1802` = 10L, `1805` = 10L, `1807` = 11L, `1808` = 11L, `1809` = 10L, `1810` = 8L, `1811` = 9L, `1812` = 11L, `1815` = 10L, `1816` = 11L, `1817` = 10L, `1819` = 10L, `1822` = 9L, `1823` = 11L, `1824` = 11L, `1826` = 4L, `1827` = 7L, `1828` = 6L, `1830` = 6L, `1832` = 6L, `1833` = 8L, `1834` = 6L, `1835` = 7L, `1836` = 7L, `1837` = 6L, `1839` = 8L, `1840` = 11L, `1841` = 8L, `1843` = 7L, `1845` = 7L, `1846` = 8L, `1848` = 8L, `1849` = 9L, `1850` = 7L, `1851` = 8L, `1852` = 9L, 0.2215, 0.1455, 0.322, 0.1065, 0.0885, 0.182, 0.0125, 0.3925, 0.4635, 0.443, 0.4595, 0.381, 0.4335, 0.194, 0.1465, 0.315, 0.066, 0.0425, 0.0165, 0.0935, 0.097, 0.073, 0.27, 0.0885, 0.134, 0.0475, 0.0335, 0.598, 0.5705, 0.403, 0.473, 0.6045, 0.579, 0.2325, 0.3005, 0.2795, 0.08, 0.56, 0.536, 0.3325, 0.4465, 0.408, 0.7415, 0.6935, 0.48, 0.61, 0.4085, 0.4215, 0.4405, 0.479, 0.458, 0.68, 0.692, 0.585, 0.5095, 0.4635, `1853` = 8L, `1855` = 8L, `1856` = 9L, `1857` = 11L, `1858` = 8L, `1860` = 10L, `1861` = 10L, `1863` = 8L, `1864` = 9L, `1865` = 9L, `1866` = 7L, `1867` = 10L, `1868` = 9L, `1869` = 8L, `1870` = 9L, `1871` = 8L, `1872` = 9L, `1873` = 9L, `1874` = 9L, `1875` = 9L, `1879` = 10L, `1880` = 9L, `1881` = 10L, `1884` = 8L, `1885` = 7L, `1886` = 9L, `1887` = 9L, `1888` = 9L, `1889` = 11L, `1892` = 9L, `1895` = 11L, `1898` = 13L, `1899` = 9L, `1900` = 9L, `1901` = 9L, `1902` = 10L, `1903` = 11L, `1904` = 10L, `1906` = 10L, `1907` = 11L, `1908` = 9L, `1909` = 9L, `1910` = 9L, `1911` = 10L, `1912` = 10L, `1913` = 10L, `1915` = 10L, `1916` = 11L, `1917` = 11L, `1918` = 10L, `1919` = 11L, `1920` = 10L, `1921` = 9L, `1922` = 10L, `1923` = 8L, `1925` = 8L, `1927` = 11L, `1928` = 9L, `1929` = 12L, `1930` = 10L, `1932` = 9L, `1933` = 11L, `1934` = 15L, `1936` = 12L, `1937` = 11L, 0.6845, 0.4935, 0.4995, 0.3895, 0.3705, 0.33, 0.313, 0.226, 0.373, 0.216, 0.3515, 0.207, 0.239, 0.222, 0.252, 0.1075, 0.0755, 0.116, 0.3165, 0.2385, 0.4355, 0.27, 0.3115, 0.1655, 0.2125, 0.39, 0.4, 0.4565, 0.41, 0.4625, 0.3475, 0.528, 0.3605, 0.5595, 0.488, 0.223, 0.518, 0.2165, 0.2465, 0.036, 0.425, 0.3935, 0.3995, 0.344, 0.38, 0.4375, 0.342, 0.2475, 0.122, 0.2815, 0.0915, 0.249, 0.095, 0.275, 0.134, 0.4765, 0.365, 0.6975, 0.581, 0.405, 0.3485, 0.51, 0.0695, 0.123, 0.293, 0.115, 0.026, 0.0215, 0.0255, 0.5425, 0.567, 0.5975, 0.545, 0.313, 0.2895, 0.151, 0.272, 0.261, 0.1565, 0.4955, 0.637, 0.5305, 0.7055, 0.3605, 0.3785, 0.429, 0.4235, 0.381, 0.5235, 0.3485, 0.44, 0.475, 0.357, 0.486, 0.5875, 0.3445, 0.4245, 0.427, 0.3665, 0.4385, 0.4385, 0.4865, 0.3325, 0.3815, 0.36, 0.3005, 0.439, 0.221, 0.249, 0.061, 0.0385, 0.1115, 0.033, 0.06, 0.0155, 0.0935, 0.0145, 0.0265, 0.0065, 0.282, 0.299, 0.192, 0.1845, 0.2095, 0.1895, 0.2, 0.043, 0.093, 0.242, 0.1585, 0.1, 0.1565, 0.099, 0.0585, 0.0315, 0.3695, 0.495, `1939` = 10L, `1940` = 11L, `1941` = 11L, `1942` = 11L, `1943` = 9L, `1944` = 11L, `1945` = 11L, `1946` = 11L, `1947` = 12L, `1948` = 12L, `1949` = 10L, `1950` = 11L, `1952` = 10L, `1953` = 10L, `1954` = 9L, `1956` = 12L, `1957` = 11L, `1958` = 16L, `1959` = 10L, `1960` = 13L, `1961` = 10L, `1963` = 10L, `1964` = 9L, `1965` = 10L, `1966` = 11L, `1971` = 11L, `1972` = 11L, `1973` = 12L, `1974` = 10L, `1976` = 11L, `1977` = 13L, `1979` = 12L, `1980` = 13L, `1981` = 10L, `1982` = 12L, `1985` = 11L, `1986` = 13L, `1987` = 4L, `1988` = 4L, `1989` = 5L, `1990` = 6L, `1991` = 7L, `1992` = 6L, `1993` = 5L, `1994` = 6L, `1995` = 6L, `1996` = 6L, `1997` = 7L, `1998` = 6L, `1999` = 7L, `2000` = 6L, `2001` = 9L, `2003` = 7L, `2004` = 6L, `2005` = 7L, `2006` = 7L, `2007` = 7L, `2010` = 8L, `2011` = 7L, `2012` = 8L, `2014` = 10L, `2015` = 10L, `2016` = 8L, `2017` = 9L, `2018` = 12L, `2020` = 9L, `2023` = 9L, `2024` = 9L, `2025` = 11L, `2026` = 10L, 0.4145, 0.335, 0.2075, 0.287, 0.2605, 0.3075, 0.4585, 0.267, 0.148, 0.1715, 0.2795, 0.2005, 0.0705, 0.1685, 0.165, 0.21, 0.1975, 0.382, 0.3785, 0.4145, 0.5275, 0.4005, 0.4055, 0.5015, 0.376, 0.6615, 0.1735, 0.28, 0.3095, 0.164, 0.138, 0.2865, 0.111, 0.3585, 0.2575, 0.3015, 0.3385, 0.305, 0.398, 0.1225, 0.1075, 0.2005, 0.16, 0.078, 0.0675, 0.0575, 0.0475, 0.185, 0.2085, 0.128, 0.1525, 0.0655, 0.0455, 0.018, 0.06, 0.2175, 0.2945, 0.1795, 0.214, 0.2655, 0.1205, 0.1255, 0.3845, 0.223, 0.1555, 0.1895, 0.066, 0.05, 0.1055, 0.081, 0.1965, 0.219, 0.4675, 0.358, 0.0735, 0.1605, 0.216, 0.193, 0.1695, 0.034, 0.0595, 0.054, 0.0945, 0.336, 0.4305, 0.5305, 0.4145, 0.597, 0.3055, 0.1145, 0.078, 0.1815, 0.2155, 0.2775, 0.1765, 0.235, 0.24, 0.225, 0.241, 0.185, 0.25, 0.0885, 0.204, 0.154, 0.3175, 0.2455, 0.344, 0.26, 0.279, 0.366, 0.0345, 0.1175, 0.0065, 0.043, 0.0545, 0.155, 0.1185, 0.1215, 0.1895, 0.191, 0.1535, `2028` = 9L, `2029` = 10L, `2030` = 9L, `2031` = 10L, `2032` = 10L, `2033` = 12L, `2034` = 10L, `2035` = 14L, `2036` = 9L, `2037` = 5L, `2038` = 5L, `2039` = 5L, `2040` = 6L, `2041` = 8L, `2042` = 8L, 0.0615, 0.0895, 0.0595, 0.104, 0.069, 0.08, 0.0475, 0.036, 0.036, 0.0095, 0.0045, 0.0065, 0.3925, 0.416, 0.3865, 0.191, 0.108, 0.2185, 0.2735, 0.261, 0.2405, 0.2825, 0.262, 0.2385, 0.2415, 0.2855, 0.143, 0.32, 0.2795, 0.275, 0.34, 0.673, 0.2485, 0.215, 0.347, 0.5295, 0.367, 0.4775, 0.348, 0.313, 0.2385, 0.6215, 0.403, 0.289, 0.3485, 0.3155, 0.2, 0.1675, 0.245, 0.189, 0.236, 0.2305, 0.2015, 0.1725, 0.246, 0.292, 0.021, 0.3295, 0.1975, 0.211, 0.2475, 0.309, 0.3145, 0.1925, 0.3275, 0.315, 0.215, 0.2805, 0.157, 0.255, 0.125, 0.1655, 0.119, 0.152, 0.094, 0.2875, 0.2125, 0.2855, 0.2665, 0.027, 0.071, 0.0765, 0.0735, 0.075, 0.0655, 0.108, 0.097, 0.086, 0.143, 0.127, 0.208, 0.175, 0.156, 0.1185, 0.2435, 0.2745, 0.1795, 0.266, 0.2945, 0.2175, 0.3065, 0.3355, 0.27, 0.3375, 0.4815, 0.369, 0.429, 0.407, 0.362, 0.349, 0.442, 0.4545, 0.5225, 0.5175, 0.549, 0.653, 0.606, 0.421, 0.5585, 0.629, 0.545, 0.5315, 0.6285, 0.6355, 0.465, 0.5895, 0.4525, 0.619, 0.668, 0.7595, 0.675, 0.586, 0.959, 0.5905, 0.5445, 0.784, 0.628, 0.861, 0.833, 0.8455, 1.1465, 0.025, 0.031, 0.0295, 0.031, 0.0455, 0.065, 0.05, 0.062, 0.053, 0.07, 0.0705, 0.0945, 0.0665, 0.104, 0.111, 0.1045, 0.155, 0.1635, 0.169, 0.157, 0.164, 0.1645, 0.217, 0.1815, 0.1825, 0.209, 0.2025, 0.1695, 0.191, 0.197, 0.2355, 0.2335, 0.2355, 0.2325, 0.2315, 0.23, 0.29, 0.222, 0.3205, 0.2925, 0.194, 0.312, 0.1995, 0.32, 0.3035, 0.272, 0.3145, 0.2835, 0.3195, 0.336, 0.3165, 0.362, 0.289, 0.25, 0.3935, 0.238, 0.347, 0.3865, 0.3695, 0.3475, 0.325, 0.328, 0.3935, 0.2855, 0.544, 0.4525, 0.3075, 0.513, 0.54, 0.5265, 0.4075, 0.4955, 0.4985, 0.5585, 0.479, 0.5155, 0.4515, 0.5865, 0.621, 0.4655, 0.5265, 0.565, 0.554, 0.585, 0.594, 0.6235, 0.5705, 0.4755, 0.573, 0.5845, 0.637, 0.599, 0.5185, 0.623, 0.684, 0.693, 0.6145, 0.779, 0.709, 0.633, 0.5475, 0.515, 0.57, 0.6665, 0.6645, 0.69, 0.6425, 0.594, 0.641, 0.682, 0.739, 0.7645, 0.7245, 0.8255, 0.8035, 0.693, 1.012, 0.7645, 0.6655, 1.1335, 1.007, 0.0135, 0.0155, 0.027, 0.03, 0.0245, 0.0365, 0.054, 0.0405, 0.055, 0.065, 0.0675, 0.09, 0.1165, 0.1165, 0.122, 0.194, 0.172, 0.132, 0.1255, 0.1255, 0.162, 0.1985, 0.222, 0.1545, 0.1945, 0.241, 0.1945, 0.18, 0.27, 0.211, 0.206, 0.2165, 0.3395, 0.235, 0.215, 0.2485, 0.2485, 0.246, 0.3465, 0.261, 0.2615, 0.215, 0.3175, 0.3525, 0.288, 0.523, 0.2625, 0.297, 0.279, 0.3125, 0.4225, 0.434, 0.4615, 0.427, 0.395, 0.411, 0.464, 0.42, 0.336, 0.43, 0.4565, 0.447, 0.412, 0.556, 0.435, 0.4245, 0.549, 0.415, 0.4765, 0.945, 0.3775, 0.486, 0.481, 0.485, 0.696, 0.5455, 0.555, 0.582, 0.433, 0.5315, 0.783, 0.498, 0.4815, 0.4615, 0.447, 0.5315, 0.4285, 0.5635, 0.7335, 0.587, 0.589, 0.5635, 0.5115, 0.632, 0.6465, 0.528, 0.6415, 0.565, 0.553, 0.5885, 0.629, 0.6035, 0.6075, 0.664, 0.699, 0.624, 0.7355, 0.7945, 0.885, 1.095, 0.6395, 0.735, 1.0465, 0.889, 0.9035, 0.691, 1.026, 0.952, 0.896, 1.109, 1.1965, 0.8295, 1.488, 0.196, 0.034, `2047` = 7L, `2048` = 6L, `2049` = 9L, `2050` = 8L, `2051` = 8L, `2052` = 8L, `2053` = 8L, `2054` = 7L, `2055` = 7L, `2056` = 9L, `2057` = 7L, `2059` = 8L, `2061` = 9L, `2062` = 8L, `2063` = 7L, `2065` = 8L, `2066` = 8L, `2067` = 10L, `2068` = 9L, `2069` = 9L, `2070` = 9L, `2071` = 11L, `2072` = 9L, `2073` = 8L, `2075` = 11L, `2076` = 9L, `2078` = 8L, `2079` = 8L, `2080` = 9L, `2081` = 9L, `2082` = 8L, `2083` = 10L, `2086` = 10L, `2088` = 11L, `2089` = 12L, 0.0295, 0.039, 0.0545, 0.044, 0.435, 0.0545, 0.046, 0.0565, 0.102, 0.077, 0.096, 0.0775, 0.0925, 0.105, 0.1, 0.0855, 0.0875, 0.081, 0.0825, 0.0945, 0.089, 0.096, 0.118, 0.093, 0.11, 0.0825, 0.0945, 0.11, 0.0865, 0.111, 0.097, 0.1025, 0.1665, 0.1135, 0.1445, 0.1205, 0.1265, 0.157, 0.168, 0.173, 0.126, 0.2065, 0.1615, 0.2115, 0.208, 0.164, 0.184, 0.358, 0.2225, 0.1575, 0.206, 0.1425, 0.175, 0.2055, 0.167, 0.2535, 0.1785, 0.211, 0.244, 0.214, 0.2185, 0.224, 0.302, 0.239, 0.259, 0.2925, 0.2595, 0.248, 0.3265, 0.32, 0.233, 0.3005, 0.4725, 0.2525, 0.3595, 0.335, 0.4315, 0.3205, 0.3535, 0.27, 0.3745, 0.2965, 0.413, 0.3285, 0.3325, 0.407, 0.3135, 0.3485, 0.3715, 0.3095, 0.5565, 0.547, 0.414, 0.4215, 0.3635, 0.352, 0.3815, 0.347, 0.383, 0.461, 0.511, 0.4415, 0.6715, 0.405, 0.4, 0.4555, 0.522, 0.48, 0.5825, 0.8, 0.5315, 0.336, 0.388, 0.4115, 0.4645, 0.508, 0.451, 0.565, 0.4755, 0.419, 0.4455, 0.6315, 0.419, 0.432, 0.5405, 0.407, 0.435, 0.4495, 0.4565, 0.494, 0.4475, 0.4845, 0.4905, 0.4795, 0.5715, 0.5765, 0.596, 0.463, 0.4955, 0.5475, 0.5715, 0.5865, 0.5945, 0.6025, 0.5065, 0.534, 0.705, 0.707, 0.5205, 0.405, 0.644, 0.4865, 0.556, 0.6165, 0.7455, 0.4785, 0.6865, 0.7245, 0.564, 0.7305, 0.6, 0.6395, 0.724, 0.615, 0.6345, 0.4375, 0.876, 0.8145, 0.7465, `2090` = 10L, `2091` = 17L, `2093` = 10L, `2094` = 11L, `2095` = 7L, `2096` = 7L, `2097` = 11L, `2099` = 8L, `2100` = 10L, `2101` = 10L, `2102` = 19L, `2103` = 9L, `2104` = 9L, `2105` = 11L, `2106` = 10L, `2107` = 14L, `2108` = 15L, `2109` = 27L, `2110` = 13L, `2111` = 5L, `2112` = 9L, `2113` = 8L, `2114` = 7L, `2115` = 4L, `2116` = 3L, `2117` = 6L, `2118` = 7L, `2120` = 9L, `2121` = 7L, `2123` = 11L, `2124` = 6L, `2125` = 7L, `2126` = 8L, `2128` = 7L, `2131` = 9L, `2134` = 9L, `2135` = 8L, `2136` = 12L, `2137` = 9L, `2139` = 16L, `2142` = 7L, `2144` = 10L, `2145` = 9L, `2146` = 9L, `2147` = 11L, `2149` = 8L, `2150` = 7L, `2151` = 10L, `2152` = 18L, `2154` = 5L, `2155` = 10L, `2156` = 16L, `2157` = 12L, `2158` = 17L, `2160` = 12L, `2161` = 19L, `2164` = 9L, `2166` = 9L, `2167` = 6L, `2168` = 5L, 1.1075, 1.0465, 0.96, 0.743, 0.913, 0.9515, 0.697, 0.6785, 0.949, 1.1565, 1.232, 0.8905, 0.105, 0.1325, 0.187, 0.143, 0.188, 0.1185, 0.112, 0.1455, 0.141, 0.2015, 0.2385, 0.164, 0.2435, 0.213, 0.1975, 0.229, 0.19, 0.2175, 0.281, 0.2795, 0.2075, 0.2945, 0.2655, 0.239, 0.361, 0.2695, 0.497, 0.254, 0.3685, 0.346, 0.398, 0.3565, 0.375, 0.343, 0.453, 0.4755, 0.504, 0.595, 0.65, 0.475, 0.4095, 0.387, 0.613, 0.453, 0.452, 0.4365, 0.4535, 0.417, 0.5125, 0.353, 0.4815, 0.451, 0.52, 0.467, 0.481, 0.6605, 0.505, 0.544, 0.6635, 0.7035, 0.6405, `2170` = 4L, `2171` = 7L, `2172` = 6L, `2173` = 5L, `2174` = 10L, `2176` = 12L, `2177` = 20L, `2178` = 14L, `2179` = 15L, `2180` = 14L, `2181` = 21L, `2182` = 16L, `2183` = 13L, `2184` = 6L, `2187` = 9L, `2188` = 14L, `2190` = 10L, `2192` = 15L, `2193` = 14L, `2194` = 6L, `2195` = 13L, `2196` = 5L, `2197` = 11L, `2199` = 6L, `2201` = 13L, `2203` = 19L, `2204` = 18L, `2205` = 7L, `2207` = 5L, `2209` = 16L, `2210` = 27L, `2211` = 18L, `2212` = 17L, `2214` = 17L, `2215` = 8L, `2217` = 7L, `2218` = 13L, `2219` = 14L, `2220` = 13L, `2221` = 8L, `2222` = 17L, `2223` = 13L, `2224` = 14L, `2225` = 9L, `2228` = 7L, `2232` = 9L, `2233` = 9L, `2234` = 17L, `2235` = 14L, `2236` = 13L, `2237` = 15L, `2238` = 17L, `2239` = 8L, `2240` = 8L, `2242` = 11L, `2243` = 7L, `2245` = 11L, `2247` = 10L, `2248` = 9L, `2249` = 6L, `2250` = 12L, `2252` = 13L, `2253` = 14L, `2254` = 11L, `2255` = 14L, `2256` = 12L, `2257` = 8L, `2258` = 13L, `2260` = 13L, `2261` = 10L, `2263` = 17L, `2265` = 14L, `2268` = 15L, `2269` = 13L, `2270` = 12L, 0.59, 0.702, 0.475, 0.695, 0.8885, 0.589, 0.5945, 0.651, 0.7665, 0.9165, 0.7535, 0.797, 1.017, 1.351, 0.9115, 0.942, 0.0415, 0.047, 0.063, 0.0715, 0.073, 0.083, 0.097, 0.0935, 0.0895, 0.108, 0.1, 0.1065, 0.141, 0.135, 0.131, 0.129, 0.181, 0.176, 0.2015, 0.188, 0.2825, 0.1965, 0.221, 0.2935, 0.204, 0.3035, 0.2075, 0.2505, 0.2455, 0.2375, 0.281, 0.3005, 0.213, 0.32, 0.238, 0.2965, 0.3525, 0.261, 0.282, 0.245, 0.1935, 0.32, 0.3935, 0.419, 0.2505, 0.2955, 0.3475, 0.3285, 0.2375, 0.358, 0.269, 0.373, 0.339, 0.3945, 0.3845, 0.3685, 0.349, 0.3265, 0.31, 0.3595, 0.4245, 0.3345, 0.3385, 0.354, 0.3705, 0.364, 0.509, 0.3275, 0.3745, 0.3475, 0.4605, 0.4005, 0.483, 0.428, 0.469, 0.303, 0.4305, 0.559, 0.428, 0.419, 0.4175, 0.463, 0.377, 0.575, 0.4135, 0.38, 0.4495, 0.504, 0.5625, 0.468, 0.379, 0.4735, 0.393, 0.7285, 0.4565, 0.4835, 0.6025, 0.449, 0.3835, 0.5225, 0.6035, 0.4935, 0.4995, 0.446, 0.582, 0.358, 0.6285, 0.464, 0.4895, 0.4645, 0.643, 0.6765, 0.438, 0.514, 0.5255, 0.6565, 0.605, 0.715, 0.611, 0.5805, 0.5385, 0.5215, 0.5475, 0.763, 0.519, 0.5645, 0.741, 0.605, 0.6665, 0.554, 0.5695, 0.615, 0.665, 0.6385, 0.586, 0.4795, 0.688, 0.667, 0.711, 0.6505, 0.7685, 0.6635, 0.6145, 0.631, 0.606, 0.6075, 0.6255, 0.711, 0.5235, 0.74, 0.572, 0.592, 0.714, 0.7185, 0.7775, 0.687, 0.8445, 0.6805, 0.9895, 0.9925, 0.9215, 1.1455, 0.631, 1.03, 0.799, 0.9655, 0.7735, 1.1155, 1.3485, 0.096, 0.272, 0.259, 0.199, 0.229, 0.4005, 0.207, 0.362, 0.295, 0.419, 0.3085, 0.3485, 0.494, 0.4185, 0.472, 0.303, 0.4955, 0.567, 0.6325, 0.5075, 0.403, 0.3945, 0.3975, 0.4975, 0.5575, 0.575, 0.511, 0.5275, 0.445, 0.533, 0.4895, 0.672, 0.777, 0.6325, 0.5595, 0.6775, 0.805, 1.0715, 0.691, 0.9285, 0.0235, 0.0565, 0.0815, 0.0815, 0.095, 0.1185, 0.103, 0.114, `2271` = 18L, `2273` = 15L, `2276` = 20L, `2277` = 14L, `2278` = 19L, `2279` = 9L, `2280` = 10L, `2282` = 8L, `2283` = 10L, `2284` = 7L, `2285` = 9L, `2287` = 9L, `2288` = 7L, `2289` = 6L, `2293` = 9L, `2294` = 6L, `2295` = 12L, `2296` = 10L, `2297` = 13L, `2298` = 8L, `2299` = 7L, `2300` = 8L, `2301` = 10L, `2303` = 9L, `2304` = 11L, `2305` = 11L, `2306` = 23L, `2308` = 16L, `2309` = 11L, `2310` = 13L, `2311` = 13L, `2312` = 7L, `2313` = 8L, `2314` = 16L, `2315` = 14L, `2316` = 17L, `2317` = 13L, `2319` = 12L, `2320` = 15L, `2321` = 10L, `2323` = 12L, `2324` = 8L, `2325` = 17L, `2326` = 10L, `2327` = 11L, `2328` = 13L, `2329` = 15L, `2331` = 9L, `2332` = 15L, `2335` = 23L, `2336` = 23L, `2337` = 18L, `2339` = 17L, `2340` = 17L, `2341` = 11L, `2342` = 7L, `2345` = 21L, `2346` = 17L, `2347` = 13L, `2348` = 11L, `2349` = 16L, `2350` = 9L, `2351` = 12L, `2352` = 19L, `2353` = 18L, `2354` = 17L, `2355` = 11L, `2356` = 13L, `2358` = 17L, `2359` = 20L, `2360` = 13L, `2361` = 11L, `2362` = 12L, `2364` = 18L, `2365` = 15L, `2366` = 12L, `2367` = 19L, `2369` = 16L, `2370` = 12L, `2371` = 8L, `2372` = 3L, `2373` = 12L, `2374` = 12L, `2375` = 12L, `2376` = 10L, `2377` = 8L, `2378` = 12L, `2379` = 12L, `2380` = 10L, `2381` = 5L, `2385` = 11L, `2386` = 12L, `2387` = 14L, `2389` = 10L, `2390` = 11L, `2391` = 16L, `2392` = 10L, `2395` = 14L, `2396` = 14L, `2397` = 14L, `2398` = 17L, `2400` = 17L, `2401` = 13L, `2403` = 16L, `2404` = 10L, `2406` = 15L, `2407` = 10L, `2408` = 12L, `2410` = 8L, `2411` = 10L, `2412` = 9L, `2413` = 7L, `2414` = 12L, `2416` = 10L, `2420` = 10L, `2422` = 15L, `2423` = 12L, `2424` = 10L, `2425` = 6L, `2426` = 13L, `2428` = 6L, `2430` = 9L, `2431` = 11L, `2432` = 18L, `2435` = 16L, `2437` = 23L, `2438` = 9L, `2439` = 16L, `2444` = 11L, `2445` = 9L, `2446` = 12L, `2447` = 14L, `2448` = 6L, `2449` = 7L, `2450` = 9L, `2451` = 10L, `2453` = 9L, `2454` = 8L, `2456` = 6L, `2457` = 5L, `2460` = 20L, `2461` = 14L, `2462` = 13L, `2463` = 8L, `2464` = 17L, `2465` = 11L, `2466` = 11L, `2467` = 9L, `2468` = 16L, `2469` = 8L, `2470` = 13L, `2471` = 11L, `2472` = 20L, `2473` = 14L, `2476` = 12L, `2477` = 18L, `2478` = 13L, `2479` = 8L, `2480` = 14L, `2481` = 13L, `2483` = 12L, `2484` = 14L, `2486` = 8L, `2487` = 13L, `2488` = 11L, 0.1405, 0.1245, 0.1125, 0.1675, 0.1395, 0.1515, 0.13, 0.1525, 0.196, 0.203, 0.2505, 0.2465, 0.1885, 0.2065, 0.181, 0.28, 0.2245, 0.334, 0.285, 0.2555, 0.2165, 0.308, 0.2445, 0.3285, 0.2555, 0.2805, 0.3045, 0.248, 0.298, 0.321, 0.247, 0.2715, 0.53, 0.305, 0.2615, 0.295, 0.2345, 0.332, 0.304, 0.382, 0.3455, 0.3135, 0.3975, 0.454, 0.4275, 0.382, 0.318, 0.495, 0.432, 0.4685, 0.3795, 0.45, 0.3375, 0.3485, 0.3665, 0.4035, 0.37, 0.518, 0.3525, 0.4315, 0.4655, 0.456, 0.386, 0.466, 0.482, 0.334, 0.479, 0.4375, 0.4255, 0.4435, 0.4975, 0.5815, `2489` = 14L, `2490` = 15L, `2492` = 10L, `2493` = 11L, `2494` = 8L, `2495` = 9L, `2496` = 12L, `2497` = 15L, `2498` = 12L, `2499` = 15L, `2501` = 12L, `2502` = 9L, `2503` = 5L, `2504` = 6L, `2505` = 6L, `2506` = 6L, `2508` = 7L, `2509` = 6L, `2511` = 8L, `2512` = 6L, `2514` = 8L, `2515` = 8L, `2517` = 10L, `2518` = 7L, `2519` = 8L, `2520` = 7L, `2521` = 9L, `2523` = 11L, `2524` = 9L, `2525` = 11L, `2527` = 9L, `2528` = 10L, `2530` = 8L, `2531` = 9L, `2532` = 10L, `2534` = 10L, `2535` = 12L, `2536` = 11L, `2539` = 15L, `2541` = 13L, `2542` = 9L, `2544` = 10L, `2545` = 10L, `2546` = 4L, `2547` = 4L, `2548` = 5L, `2549` = 4L, `2550` = 5L, `2551` = 5L, `2552` = 6L, `2553` = 6L, `2558` = 7L, `2559` = 6L, `2560` = 7L, `2561` = 7L, `2562` = 6L, `2563` = 6L, `2564` = 7L, `2566` = 8L, `2567` = 7L, `2569` = 7L, `2570` = 6L, `2571` = 7L, `2572` = 9L, `2573` = 7L, `2574` = 8L, `2575` = 8L, `2576` = 7L, `2578` = 7L, `2579` = 9L, `2580` = 7L, `2581` = 8L, `2582` = 6L, `2583` = 6L, `2584` = 8L, `2585` = 9L, `2586` = 8L, `2588` = 9L, `2590` = 7L, `2591` = 8L, 0.564, 0.4605, 0.372, 0.579, 0.425, 0.487, 0.52, 0.46, 0.4845, 0.475, 0.63, 0.5635, 0.5065, 0.594, 0.5435, 0.6535, 0.5055, 0.4765, 0.4945, 0.488, 0.477, 0.7815, 0.6525, 0.616, 0.5495, 0.483, 0.531, 0.5855, 0.53, 0.5925, 0.5055, 0.556, 0.6295, 0.7165, 0.671, 0.6655, 0.6645, 0.6565, 0.675, 0.929, 0.732, 0.83, 0.801, 0.0125, 0.0075, 0.017, 0.053, 0.036, 0.031, 0.048, 0.056, 0.0375, 0.0585, 0.0875, 0.0695, 0.0725, 0.0715, 0.1425, 0.0905, 0.105, 0.109, 0.158, 0.128, 0.1675, 0.159, `2592` = 8L, `2593` = 7L, `2594` = 9L, `2595` = 9L, `2598` = 8L, `2599` = 9L, `2600` = 8L, `2601` = 10L, `2602` = 10L, `2603` = 8L, `2604` = 9L, `2605` = 10L, `2606` = 9L, `2607` = 10L, `2608` = 9L, `2610` = 11L, `2612` = 8L, `2613` = 11L, `2614` = 11L, `2616` = 12L, `2617` = 9L, `2618` = 9L, `2619` = 13L, `2621` = 10L, `2622` = 12L, `2623` = 13L, `2624` = 10L, `2626` = 10L, `2627` = 4L, `2628` = 5L, `2629` = 5L, `2630` = 6L, `2631` = 6L, `2632` = 6L, `2634` = 6L, `2636` = 7L, `2637` = 7L, `2638` = 7L, `2639` = 7L, `2640` = 6L, `2641` = 7L, `2644` = 8L, `2647` = 7L, `2648` = 8L, `2649` = 9L, `2650` = 7L, `2651` = 8L, `2652` = 8L, `2653` = 9L, `2656` = 7L, `2657` = 7L, `2658` = 9L, `2660` = 8L, `2661` = 8L, `2662` = 9L, `2664` = 9L, `2665` = 9L, `2666` = 8L, `2667` = 8L, `2668` = 7L, `2669` = 8L, `2670` = 8L, `2671` = 9L, `2672` = 8L, `2673` = 8L, `2674` = 9L, `2675` = 9L, `2676` = 10L, `2677` = 10L, `2678` = 9L, `2679` = 7L, `2680` = 9L, `2682` = 9L, `2683` = 10L, `2685` = 9L, `2687` = 11L, `2688` = 10L, `2689` = 8L, `2690` = 9L, `2691` = 9L, `2692` = 9L, `2693` = 9L, `2694` = 9L, `2695` = 10L, `2696` = 9L, `2698` = 10L, `2699` = 9L, `2700` = 11L, `2701` = 13L, `2702` = 13L, `2703` = 11L, `2704` = 11L, `2705` = 10L, `2706` = 13L, `2707` = 11L, `2708` = 9L, `2709` = 11L, `2710` = 12L, `2712` = 3L, `2715` = 5L, `2717` = 6L, `2718` = 6L, `2719` = 7L, `2720` = 5L, `2721` = 7L, `2723` = 7L, `2726` = 8L, `2727` = 7L, `2728` = 7L, `2729` = 8L, 0.2575, 0.2395, 0.167, 0.268, 0.3315, 0.288, 0.2875, 0.325, 0.2635, 0.356, 0.381, 0.4345, 0.3875, 0.513, 0.5165, 0.37, 0.4295, 0.465, 0.5255, 0.7345, 0.04, 0.0475, 0.072, 0.051, 0.072, 0.0565, 0.165, 0.1895, 0.162, 0.152, 0.2075, 0.332, 0.2435, 0.235, 0.2665, 0.2005, 0.3165, 0.25, 0.361, 0.277, 0.3135, 0.355, 0.3125, 0.3265, 0.368, 0.3945, 0.4455, 0.2925, 0.393, 0.418, 0.4145, 0.4335, 0.438, 0.5575, 0.493, 0.603, 0.6165, 0.818, 0.747, 0.9155, 0.8565, 0.6555, 0.735, 0.2995, 0.243, 0.114, 0.1545, 0.273, 0.1885, 0.1475, 0.1025, 0.2655, 0.124, 0.1175, 0.2175, 0.434, 0.416, 0.562, 0.7535, 0.5185, 0.076, 0.2165, 0.3265, 0.0855, 0.005, 0.0125, 0.0675, 0.1405, 0.201, 0.1365, 0.192, 0.113, 0.147, 0.1685, 0.106, 0.462, 0.2, 0.143, 0.3055, 0.361, 0.405, 0.076, 0.2025, 0.1575, 0.1405, 0.2015, 0.1305, 0.0465, 0.1805, 0.8435, 0.0545, 0.368, 0.473, 0.4915, 0.643, 0.61, 0.732, 0.377, 0.414, 0.115, 0.104, 0.0055, 0.055, 0.0155, 0.0115, 0.3245, 0.342, 0.414, 0.3, 0.358, 0.462, 0.358, 0.3355, 0.317, 0.2345, 0.295, 0.2, 0.243, 0.5265, 0.419, 0.0205, 0.1355, 0.037, 0.0905, 0.0265, 0.5195, 0.3445, 0.5305, 0.175, 0.0515, 0.5115, 0.3245, 0.4865, 0.387, 0.5635, 0.165, 0.0765, 0.276, 0.3285, 0.2385, 0.3395, 0.401, 0.4505, 0.296, 0.297, 0.0305, 0.2675, 0.3575, 0.419, 0.3305, 0.4125, 0.422, 0.3905, 0.225, 0.2105, 0.152, 0.1735, 0.0935, 0.12, 0.2295, 0.083, 0.345, 0.4995, 0.4125, 0.639, 0.8005, 0.216, 0.2505, 0.264, 0.506, 0.3155, 0.312, 0.2745, 0.391, 0.3465, 0.3725, 0.511, 0.6535, 0.487, 0.403, 0.4345, 0.3725, 0.3305, 0.25, 0.3125, 0.221, 0.1605, 0.3235, 0.1365, 0.068, 0.1545, 0.076, 0.3335, 0.3325, 0.322, 0.2825, 0.167, 0.2755, 0.277, 0.2505, 0.399, 0.422, 0.302, 0.2225, 0.2, 0.2145, 0.143, 0.1365, 0.0545, 0.4035, 0.391, 0.293, 0.3415, 0.251, `2730` = 7L, `2731` = 6L, `2732` = 8L, `2733` = 8L, `2735` = 8L, `2736` = 8L, `2739` = 8L, `2740` = 7L, `2741` = 8L, `2742` = 8L, `2743` = 8L, `2744` = 7L, `2745` = 11L, `2746` = 8L, `2748` = 10L, `2749` = 9L, `2751` = 8L, `2754` = 8L, `2755` = 8L, `2756` = 10L, `2757` = 8L, `2758` = 9L, `2759` = 10L, `2760` = 8L, `2761` = 10L, `2762` = 10L, `2763` = 9L, `2764` = 10L, `2765` = 9L, `2766` = 11L, `2767` = 8L, `2768` = 10L, `2769` = 11L, `2770` = 11L, `2771` = 10L, 0.2695, 0.1145, 0.3275, 0.111, 0.5775, 0.12, 0.0925, 0.2465, 0.3345, 0.1515, 0.327, 0.933, 0.425, 0.3885, 0.6275, 0.566, 0.355, 0.0605, 0.472, 0.638, 0.313, 0.3035, 0.376, 0.2035, 0.2765, 0.3815, 0.548, 0.637, 0.4825, 0.3805, 0.391, 0.535, 0.601, 0.2985, 0.343, 0.4635, 0.392, 0.4595, 0.8665, 0.8045, 0.3545, 0.1625, 0.0085, 0.145, 0.253, 0.1555, 0.0725, 0.154, 0.2745, 0.1405, 0.096, 0.011, 0.165, 0.1905, 0.2595, 0.2495, 0.165, 0.1585, 0.11, 0.55, 0.4435, 0.404, 0.5805, 0.35, 0.163, 0.159, 0.0535, 0.5265, 0.3515, 0.572, 0.371, 0.2635, 0.2165, 0.0525, 0.2935, 0.1135, 0.192, 0.2105, 0.1905, 0.1255, 0.044, 0.358, 0.0445, 0.081, 0.1775, 0.529, 0.5485, 0.2515, 0.062, 0.258, 0.1785, 0.2125, 0.2295, 0.2875, 0.036, 0.0745, 0.1125, 0.186, 0.0975, 0.0395, 0.023, 0.028, 0.3975, 0.2025, 0.2625, 0.144, 0.312, 0.276, 0.275, 0.142, 0.3025, 0.093, 0.272, 0.474, 0.393, 0.6745, 0.38, 0.5915, 0.377, 0.1525, 0.2025, 0.207, 0.2745, 0.275, 0.077, 0.21, 0.5115, 0.246, 0.242, 0.1905, 0.167, 0.115, 0.096, 0.174, 0.215, 0.2715, 0.2015, 0.219, 0.0625, 0.044, 0.052, 0.118, 0.0745, 0.2285, 0.1405, 0.1745, 0.159, 0.259, 0.25, 0.2505, 0.3445, 0.37, 0.267, 0.4625, 0.387, 0.4475, 0.48, 0.5685, 0.482, 0.6235, 0.587, 0.5395, 0.5785, 0.7225, 0.593, 0.733, 0.694, 0.8715, 0.707, `2773` = 10L, `2774` = 11L, `2775` = 9L, `2777` = 10L, `2778` = 9L, `2779` = 8L, `2781` = 10L, `2782` = 10L, `2783` = 8L, `2784` = 11L, `2785` = 9L, `2787` = 10L, `2788` = 11L, `2789` = 10L, `2790` = 9L, `2791` = 10L, `2792` = 10L, `2793` = 10L, `2794` = 12L, `2795` = 10L, `2796` = 12L, `2797` = 10L, `2798` = 11L, `2800` = 10L, `2803` = 9L, `2804` = 12L, `2805` = 9L, `2806` = 11L, `2807` = 9L, `2809` = 9L, `2810` = 11L, `2811` = 10L, `2812` = 9L, `2813` = 5L, `2814` = 4L, `2816` = 7L, `2817` = 6L, `2818` = 7L, `2819` = 8L, `2822` = 7L, `2824` = 7L, `2825` = 7L, `2828` = 8L, `2829` = 9L, `2830` = 7L, `2831` = 9L, `2832` = 7L, `2833` = 9L, `2835` = 8L, `2836` = 8L, `2837` = 9L, `2838` = 8L, `2839` = 9L, `2840` = 10L, `2841` = 9L, `2842` = 9L, `2843` = 9L, `2844` = 10L, `2845` = 10L, `2846` = 10L, `2847` = 10L, `2850` = 10L, `2851` = 10L, `2854` = 8L, `2856` = 11L, `2857` = 11L, `2858` = 11L, `2859` = 11L, `2860` = 11L, `2861` = 10L, `2863` = 10L, `2864` = 11L, `2865` = 6L, `2866` = 4L, `2867` = 5L, `2868` = 6L, `2872` = 7L, `2873` = 8L, `2874` = 6L, `2875` = 8L, `2876` = 7L, `2877` = 7L, `2878` = 9L, `2879` = 8L, `2880` = 8L, `2882` = 8L, `2884` = 8L, `2885` = 9L, `2886` = 8L, `2887` = 8L, `2888` = 9L, `2891` = 8L, `2893` = 10L, `2894` = 8L, `2895` = 11L, `2897` = 8L, `2898` = 10L, `2899` = 8L, `2900` = 8L, `2901` = 9L, `2902` = 8L, `2903` = 9L, `2904` = 8L, `2905` = 9L, `2907` = 8L, `2908` = 10L, `2910` = 9L, `2911` = 11L, `2912` = 11L, `2915` = 9L, `2916` = 10L, `2917` = 10L, `2919` = 9L, `2920` = 10L, `2921` = 10L, 1.0685, 0.02, 0.024, 0.0215, 0.0345, 0.0415, 0.066, 0.0515, 0.065, 0.1155, 0.1575, 0.1445, 0.175, 0.269, 0.185, 0.231, 0.2005, 0.1945, 0.198, 0.1895, 0.23, 0.231, 0.311, 0.201, 0.243, 0.274, 0.239, 0.285, 0.394, 0.4035, 0.41, 0.3095, 0.4055, 0.33, 0.5475, 0.504, 0.429, 0.41, 0.405, 0.4695, 0.542, 0.491, 0.6025, 0.5335, 0.564, 0.5005, 0.6, 0.5085, 0.5415, 0.545, 0.6865, 0.671, 0.477, 0.659, 0.604, 0.617, 0.556, 0.6735, 0.741, 0.8115, 0.7855, 0.767, 0.696, 0.7295, 0.9915, 1.0515, 0.0215, 0.495, 0.0415, 0.0745, 0.0955, 0.0925, 0.1315, 0.1635, 0.209, 0.173, 0.1525, 0.2645, 0.2085, 0.266, 0.233, 0.294, 0.273, 0.377, 0.2465, 0.3095, 0.265, 0.3745, 0.403, 0.368, 0.434, 0.2835, 0.3495, 0.546, 0.3875, 0.4235, 0.41, 0.467, 0.3085, 0.413, 0.496, 0.445, 0.5415, 0.4915, 0.4215, 1.005, 0.4505, 0.543, 0.707, 0.5885, 0.505, 0.4965, 0.484, 0.6325, 0.6575, 0.537, 0.506, 0.6055, 0.5775, 0.5235, 0.615, 0.6095, 0.7215, 0.694, 0.5985, 0.576, 0.926, 0.7105, 0.724, 0.7635, 0.8745, 0.693, 0.83, 0.8735, 0.9095, 0.8915, 0.0125, 0.0425, 0.1005, 0.075, 0.078, 0.074, 0.096, 0.095, 0.149, 0.176, 0.0965, 0.416, 0.1145, 0.138, 0.124, 0.1325, `2924` = 13L, `2926` = 10L, `2927` = 11L, `2928` = 12L, `2929` = 11L, `2930` = 11L, `2931` = 11L, `2934` = 10L, `2935` = 9L, `2936` = 10L, `2937` = 11L, `2938` = 10L, `2939` = 11L, `2941` = 10L, `2943` = 9L, `2944` = 10L, `2945` = 7L, `2946` = 11L, `2947` = 11L, `2948` = 10L, `2951` = 11L, `2954` = 11L, `2955` = 12L, `2956` = 15L, `2957` = 12L, `2958` = 11L, `2959` = 12L, `2961` = 10L, `2962` = 10L, `2964` = 12L, `2967` = 10L, `2968` = 11L, `2972` = 11L, `2974` = 12L, `2975` = 12L, `2976` = 6L, `2977` = 8L, `2978` = 6L, `2979` = 8L, `2980` = 8L, `2981` = 7L, `2982` = 8L, `2983` = 9L, `2984` = 8L, `2988` = 10L, `2989` = 9L, `2990` = 9L, `2992` = 8L, `2993` = 8L, `2996` = 9L, `2997` = 10L, `2999` = 9L, `3000` = 11L, `3001` = 9L, `3002` = 11L, `3004` = 10L, `3005` = 11L, `3006` = 13L, `3008` = 14L, `3011` = 6L, `3012` = 8L, `3014` = 8L, `3016` = 6L, `3017` = 7L, `3018` = 8L, `3019` = 9L, `3020` = 8L, `3021` = 8L, `3022` = 9L, `3023` = 8L, 0.1295, 0.1855, 0.2235, 0.1615, 0.261, 0.252, 0.187, 0.279, 0.2535, 0.2215, 0.252, 0.245, 0.3985, 0.2415, 0.2355, 0.329, 0.345, 0.3045, 0.3285, 0.366, 0.3025, 0.4325, 0.495, 0.257, 0.481, 0.262, 0.551, 0.402, 0.329, 0.332, 0.5265, 0.5405, 0.47, 0.3645, 0.4025, 0.2895, 0.411, 0.4625, 0.4925, 0.457, 0.5055, 0.4325, 0.6895, 0.389, 0.4935, 0.6005, 0.5325, 0.5445, 0.5175, 0.555, 0.452, 0.539, 0.4935, 0.5565, 0.5395, 0.6185, 0.6485, 0.6255, 0.6525, 0.8365, 0.8265, 0.706, 1.1055, 1.253, `3024` = 8L, `3025` = 9L, `3026` = 7L, `3027` = 8L, `3028` = 8L, `3029` = 7L, `3030` = 11L, `3031` = 11L, `3032` = 8L, `3033` = 9L, `3034` = 8L, `3035` = 10L, `3036` = 10L, `3037` = 11L, `3039` = 9L, `3041` = 10L, `3043` = 8L, `3044` = 10L, `3045` = 9L, `3047` = 11L, `3050` = 9L, `3051` = 12L, `3052` = 9L, `3053` = 9L, `3055` = 12L, `3056` = 11L, `3057` = 11L, `3059` = 11L, `3060` = 11L, `3061` = 11L, `3062` = 10L, `3063` = 11L, `3065` = 9L, `3066` = 11L, `3067` = 9L, `3068` = 11L, `3069` = 12L, `3070` = 11L, `3071` = 9L, `3072` = 11L, 0.0205, 0.0285, 0.098, 0.072, 0.0945, 0.12, 0.117, 0.213, 0.2365, 0.2235, 0.4515, 0.424, 0.4325, 0.402, 0.38, 0.3695, 0.416, 0.49, 0.484, 0.6245, 0.465, 0.4535, 0.4255, 0.5105, 0.539, 0.59, 0.535, 0.656, 0.575, 0.623, 0.5015, 0.524, 0.8485, 0.831, 0.768, 0.8465, 0.637, 1.1705, 1.1495, 0.0505, 0.051, 0.088, 0.075, 0.0855, 0.151, 0.1, 0.15, 0.109, 0.1555, 0.161, 0.165, 0.264, 0.2855, 0.249, 0.245, 0.2255, 0.25, 0.231, `3073` = 10L, `3074` = 10L, `3075` = 11L, `3076` = 11L, `3077` = 11L, `3078` = 10L, `3079` = 11L, `3082` = 14L, `3083` = 11L, `3084` = 4L, `3085` = 5L, `3086` = 6L, `3087` = 7L, `3088` = 7L, `3089` = 8L, `3092` = 10L, `3093` = 11L, `3094` = 11L, `3096` = 9L, `3097` = 9L, `3099` = 8L, `3101` = 10L, `3102` = 11L, `3103` = 8L, `3105` = 10L, `3106` = 5L, `3107` = 5L, `3108` = 5L, `3110` = 7L, `3111` = 8L, 0.456, 0.352, 0.417, 0.3675, 0.358, 0.3525, 0.34, 0.2695, 0.347, 0.3355, 0.354, 0.455, 0.495, 0.371, 0.5205, 0.393, 0.4315, 0.363, 0.334, 0.4675, 0.492, 0.445, 0.4575, 0.617, 0.4365, 0.425, 0.507, 0.469, 0.516, 0.5865, 0.559, 0.5415, 0.455, 0.5935, 0.531, 0.667, 0.5725, 0.456, 0.618, 0.548, 0.4915, 0.634, 0.6, 0.4785, 0.59, 0.526, 0.6115, 0.693, 0.753, 0.6215, 0.7255, 0.7765, 0.675, 0.5755, 0.739, 0.618, 0.938, 0.909, 0.954, 0.14, 0.212, 0.254, 0.4235, 0.465, 0.306, 0.391, 0.508, 0.4065, 0.6115, 0.4625, 0.509, 0.5305, 0.4525, 0.597, 0.5475, 0.532, 0.634, 0.456, 0.678, 0.6205, 0.6605, 0.8345, 1.2395, 0.09, 0.106, 0.0845, 0.115, 0.156, 0.136, 0.1755, 0.14, 0.171, 0.138, 0.1485, 0.2265, 0.182, 0.177, 0.241, 0.226, 0.237, 0.2775, 0.3155, 0.3015, 0.273, 0.3125, 0.293, 0.3015, 0.3045, 0.3305, 0.385, 0.2895, 0.325, 0.45, 0.4925, 0.326, 0.382, 0.4095, 0.4005, 0.3695, 0.469, 0.422, 0.371, 0.4875, 0.492, 0.495, 0.577, 0.4405, 0.5525, 0.559, 0.695, 0.6345, 0.6955, 0.5455, 0.587, 0.7765, 0.5325, 0.557, 0.615, 0.58, 0.686, 0.667, 0.5415, 1.0135, 0.019, 0.026, 0.039, 0.3045, 0.139, 0.1235, 0.197, 0.244, 0.302, 0.3355, 0.249, 0.2635, 0.446, 0.43, 0.5145, 0.6065, 0.053, 0.059, 0.1505, `3112` = 8L, `3113` = 7L, `3114` = 7L, `3115` = 7L, `3117` = 10L, `3119` = 8L, `3124` = 9L, `3126` = 10L, `3127` = 11L, `3128` = 9L, `3129` = 10L, `3131` = 11L, `3132` = 11L, `3133` = 10L, `3134` = 9L, `3135` = 8L, `3136` = 9L, `3137` = 11L, `3138` = 10L, `3139` = 10L, `3140` = 11L, `3141` = 20L, `3142` = 5L, `3143` = 5L, `3145` = 9L, `3146` = 13L, `3147` = 14L, `3148` = 13L, `3151` = 10L, `3152` = 21L, `3153` = 11L, `3155` = 7L, `3156` = 9L, `3158` = 9L, `3159` = 9L, `3161` = 7L, `3162` = 6L, `3163` = 15L, `3164` = 9L, `3165` = 13L, 0.088, 0.1265, 0.1605, 0.1215, 0.1815, 0.1725, 0.436, 0.3295, 0.354, 0.581, 0.435, 0.5105, 0.536, 0.526, 0.74, 0.7385, 0.2215, 0.2165, 0.1885, 0.312, 0.188, 0.259, 0.078, 0.2885, 0.0145, 0.0265, 0.6075, 0.336, 0.4315, 0.5345, 0.5835, 0.595, 0.232, 0.2375, 0.29, 0.1575, 0.3415, 0.0565, 0.076, 0.4445, 0.33, 0.4365, 0.29, 0.503, 0.3595, 0.3245, 0.316, 0.147, 0.291, 0.106, 0.045, 0.2245, 0.404, 0.237, 0.282, 0.8835, 0.011, 0.5665, 0.3105, 0.354, 0.0605, 0.056, 0.1865, 0.2395, 0.271, 0.1115, 0.5095, 0.501, 0.456, 0.037, 0.2105, 0.3785, 0.4535, 0.3745, 0.33, 0.452, 0.531, 0.149, 0.4435, 0.216, 0.2985, 0.258, 0.1425, 0.1645, 0.231, 0.1575, 0.4835, 0.387, 0.5215, 0.596, 0.0865, 0.428, 0.6375, 0.6935, 0.377, 0.6265, 0.5825, 0.396, 0.7785, 0.694, 0.342, 0.464, 0.402, 0.0725, 0.197, 0.233, 0.3, 0.1705, 0.194, 0.0605, 0.375, 0.3185, 0.2155, 0.6145, 0.3345, 0.58, 0.398, 0.5405, 0.2, 0.3005, 0.2105, 0.1415, 0.402, 0.3195, 0.169, 0.192, 0.265, 0.215, 0.2, 0.213, 0.493, 0.533, 0.414, 0.305, 0.275, 0.262, 0.2845, 0.4905, 0.3345, 0.437, 0.4325, 0.4445, 0.6525, 0.6465, 0.3235, 0.383, 0.1805, 0.037, 0.21, 0.483, 0.1915, 0.243, 0.138, 0.184, 0.0745, 0.034, 0.4715, 0.302, 0.198, 0.1435, 0.1165, 0.0615, 0.6255, 0.3775, 0.2905, 0.146, 0.312, 0.1735, 0.191, 0.1465, 0.185, 0.6025, 0.5415, 0.3465, 0.4285, 0.215, 0.1315, 0.166, 0.175, 0.232, 0.1215, 0.3265, 0.1665, 0.1365, 0.171, 0.206, 0.1685, 0.0415, 0.4005, 0.124, 0.307, 0.0655, 0.134, 0.3685, 0.41, 0.169, 0.0615, 0.3225, 0.2085, 0.048, 0.0615, 0.038, 0.3245, 0.2605, 0.3055, 0.194, 0.341, 0.3035, 0.734, 0.377, 0.5035, 0.353, 0.5835, 0.368, 0.412, 0.5495, 0.0855, 0.19, 0.2145, 0.1685, 0.066, 0.0815, 0.0785, 0.082, 0.081, 0.147, 0.131, 0.167, 0.3325, 0.249, 0.432, 0.491, 0.4945, 0.539, 0.444, 0.5195, 0.882, 1.1455, 0.0345, 0.0425, 0.065, 0.0915, 0.1255, 0.0945, 0.134, 0.188, 0.21, 0.234, 0.1955, 0.349, 0.219, 0.3015, 0.2555, 0.2885, 0.4035, 0.3205, 0.3645, `3166` = 9L, `3168` = 15L, `3170` = 10L, `3171` = 14L, `3172` = 13L, `3175` = 9L, `3178` = 15L, `3179` = 8L, `3180` = 8L, `3182` = 11L, `3184` = 11L, `3187` = 6L, `3188` = 8L, `3189` = 16L, `3191` = 5L, `3192` = 13L, `3193` = 14L, `3194` = 20L, `3197` = 5L, `3198` = 7L, `3199` = 11L, `3200` = 9L, `3201` = 10L, `3202` = 6L, `3203` = 17L, `3204` = 17L, `3205` = 15L, `3207` = 8L, `3208` = 15L, `3211` = 12L, `3212` = 10L, `3213` = 14L, `3214` = 13L, `3215` = 14L, `3216` = 11L, `3218` = 10L, `3219` = 14L, `3221` = 11L, `3223` = 18L, `3224` = 8L, `3226` = 9L, `3227` = 10L, `3228` = 13L, `3229` = 9L, `3230` = 12L, `3231` = 11L, `3234` = 12L, `3236` = 14L, `3237` = 8L, `3238` = 18L, `3239` = 14L, `3240` = 15L, `3241` = 15L, `3242` = 14L, `3243` = 15L, 0.43, 0.4445, 0.4985, 0.502, 0.598, 0.4925, 0.6715, 0.5235, 0.4495, 0.5855, 0.583, 0.4915, 0.5815, 0.7295, 0.8655, 0.8045, 0.116, 0.104, 0.1375, 0.2025, 0.242, 0.156, 0.167, 0.35, 0.38, 0.509, 0.2375, 0.4645, 0.4165, 0.4155, 0.4055, 0.3695, 0.3945, 0.4845, 0.38, 0.491, 0.4475, 0.5045, 0.5315, 0.4695, 0.592, 0.4715, 0.605, 0.554, 0.4865, 0.667, 0.7115, 0.7285, 0.556, 0.8005, 0.6425, 0.7755, 0.8605, 0.8985, 0.871, 0.7925, 0.0415, 0.0615, 0.079, 0.097, 0.0775, 0.096, 0.108, 0.1005, 0.1545, 0.151, 0.1245, 0.1695, 0.119, 0.1775, 0.1415, 0.195, 0.1795, 0.2155, 0.2375, 0.2385, 0.229, 0.2575, 0.3055, 0.296, 0.3165, 0.268, 0.31, 0.412, 0.5305, 0.6355, 0.3535, 0.349, 0.4555, 0.3605, 0.3605, 0.4135, 0.444, 0.54, 0.4035, 0.486, 0.3425, 0.4225, 0.522, 0.4905, 0.3545, 0.4835, 0.5795, 0.605, 0.564, 0.517, 0.631, 0.4265, 0.75, 0.658, 0.689, 0.58, 0.686, 0.583, 0.6485, 0.749, 0.6025, 0.647, 0.708, 1.2395, 0.0095, 0.1245, 0.31, 0.2865, 0.409, 0.3935, 0.4635, 0.423, 0.7045, 0.4375, 0.5795, 0.4955, 0.565, 0.583, 0.6035, 0.7395, 0.573, `3244` = 14L, `3245` = 19L, `3246` = 13L, `3247` = 13L, `3248` = 15L, `3249` = 11L, `3250` = 6L, `3251` = 9L, `3252` = 8L, `3253` = 12L, `3254` = 6L, `3255` = 7L, `3256` = 5L, `3257` = 12L, `3258` = 9L, `3259` = 7L, `3260` = 18L, `3261` = 13L, `3262` = 12L, `3264` = 17L, `3265` = 12L, `3266` = 14L, `3267` = 11L, `3268` = 10L, `3269` = 11L, `3270` = 12L, `3271` = 13L, `3273` = 11L, `3274` = 13L, `3275` = 18L, `3276` = 12L, `3277` = 12L, `3278` = 15L, `3280` = 18L, `3281` = 24L, `3283` = 13L, `3288` = 12L, `3290` = 15L, `3291` = 12L, `3292` = 9L, `3295` = 14L, `3296` = 14L, `3297` = 14L, `3300` = 16L, `3301` = 16L, `3302` = 11L, `3303` = 15L, `3305` = 16L, `3306` = 16L, `3308` = 9L, `3309` = 5L, `3310` = 9L, `3311` = 13L, `3312` = 12L, `3313` = 17L, `3314` = 11L, `3315` = 11L, `3316` = 9L, `3318` = 7L, `3320` = 19L, `3321` = 16L, `3322` = 11L, `3324` = 12L, `3325` = 12L, `3326` = 10L, 0.678, 0.95, 0.03, 0.0425, 0.0585, 0.057, 0.052, 0.0725, 0.135, 0.1335, 0.123, 0.1495, 0.169, 0.1515, 0.2375, 0.1525, 0.1645, 0.1895, 0.1905, 0.163, 0.213, 0.2985, 0.3905, 0.256, 0.346, 0.2975, 0.336, 0.349, 0.2945, 0.3535, 0.3445, 0.3315, 0.345, 0.437, 0.4325, 0.326, 0.434, 0.5005, 0.4565, 0.49, 0.492, 0.4305, 0.517, 0.4525, 0.5675, 0.726, 0.447, 0.481, 0.5515, 0.6445, 0.6215, 0.5985, 0.69, 0.4955, 0.575, 0.5975, 0.622, 0.8505, 0.5605, 0.532, 0.6435, 0.7035, 0.809, 0.62, `3328` = 16L, `3329` = 13L, `3330` = 10L, `3331` = 10L, `3332` = 11L, `3333` = 13L, `3334` = 12L, `3336` = 12L, `3337` = 11L, `3338` = 15L, `3339` = 16L, `3340` = 12L, `3341` = 17L, `3342` = 12L, `3343` = 14L, `3344` = 13L, `3345` = 13L, `3346` = 12L, `3348` = 11L, `3349` = 13L, `3350` = 10L, `3351` = 11L, `3354` = 8L, `3355` = 10L, `3356` = 10L, `3359` = 5L, `3361` = 19L, `3363` = 9L, `3364` = 10L, `3365` = 11L, `3366` = 11L, `3368` = 17L, `3370` = 17L, `3371` = 12L, `3372` = 10L, `3374` = 19L, `3376` = 10L, `3377` = 6L, `3379` = 8L, `3380` = 8L, 0.89, 0.637, 0.655, 0.612, 1.1945, 0.0685, 0.1725, 0.165, 0.1765, 0.4145, 0.45, 0.332, 0.3445, 0.3845, 0.3435, 0.3415, 0.502, 0.914, 0.533, 0.4895, 0.476, 0.556, 0.512, 0.694, 0.6065, 0.819, 0.041, 0.1, 0.118, 0.17, 0.195, 0.191, 0.2425, 0.234, 0.3395, 0.307, 0.2975, 0.294, 0.2025, 0.2435, 0.3065, `3382` = 19L, `3383` = 15L, `3384` = 14L, `3386` = 13L, `3387` = 13L, `3388` = 18L, `3389` = 13L, `3390` = 16L, `3391` = 10L, `3392` = 13L, `3393` = 19L, `3394` = 10L, `3395` = 13L, `3397` = 18L, `3399` = 9L, `3400` = 8L, `3402` = 18L, `3403` = 13L, `3404` = 9L, `3405` = 8L, `3406` = 7L, `3407` = 8L, `3408` = 6L, `3409` = 7L, `3410` = 7L, `3411` = 6L, `3414` = 9L, `3415` = 7L, `3416` = 9L, `3418` = 9L, `3419` = 8L, `3420` = 8L, `3421` = 10L, `3422` = 11L, `3427` = 13L, `3428` = 13L, `3429` = 4L, `3430` = 6L, `3431` = 6L, `3434` = 6L, `3435` = 6L, `3437` = 6L, `3438` = 8L, `3439` = 6L, `3440` = 8L, 0.346, 0.3345, 0.3345, 0.2815, 0.516, 0.333, 0.321, 0.442, 0.437, 0.526, 0.4985, 0.49, 0.527, 0.5995, 0.407, 0.433, `3441` = 7L, `3442` = 7L, `3443` = 8L, `3444` = 7L, `3447` = 8L, `3448` = 8L, `3449` = 7L, `3450` = 7L, `3451` = 7L, `3452` = 8L, `3453` = 8L, `3454` = 8L, `3455` = 8L, `3456` = 9L, `3457` = 12L, `3459` = 8L, `3460` = 8L, `3461` = 9L, `3462` = 11L, `3464` = 11L, `3465` = 10L, `3466` = 9L, `3468` = 10L, `3469` = 11L, `3470` = 10L, `3472` = 9L, `3476` = 6L, `3477` = 7L, `3478` = 6L, `3479` = 7L, `3482` = 6L, `3483` = 7L, `3484` = 7L, `3485` = 8L, `3486` = 7L, `3487` = 8L, `3489` = 8L, `3492` = 8L, `3493` = 8L, `3494` = 8L, `3495` = 9L, `3496` = 8L, `3497` = 8L, `3498` = 9L, `3499` = 10L, 0.5105, 0.528, 0.6275, 0.678, 0.6275, 0.6645, 0.686, 0.675, 0.607, 0.0235, 0.0595, 0.0615, 0.11, 0.173, 0.1645, 0.318, 0.2855, 0.221, 0.4225, 0.1805, 0.2725, 0.289, 0.406, 0.4275, 0.4815, 0.535, 0.4605, 0.5985, 0.5565, 0.5375, 0.256, 0.165, 0.0855, 0.175, 0.146, 0.012, 0.0795, 0.372, 0.375, 0.255, 0.3915, 0.44, 0.326, 0.096, 0.2495, 0.6175, 0.1255, 0.2425, 0.446, 0.236, 0.071, 0.443, 0.447, 0.3575, 0.4155, 0.266, `3500` = 9L, `3501` = 8L, `3502` = 12L, `3503` = 10L, `3504` = 11L, `3505` = 10L, `3506` = 10L, `3507` = 9L, `3508` = 11L, `3509` = 9L, `3510` = 10L, `3511` = 9L, `3513` = 11L, `3514` = 9L, `3515` = 10L, 0.508, 0.058, 0.285, 0.289, 0.2505, 0.2455, 0.2875, 0.191, 0.056, 0.2925, 0.587, 0.18, 0.5655, 0.439, 0.2945, 0.1525, 0.4605, 0.2955, 0.342, 0.273, 0.1535, 0.263, 0.4405, 0.7035, 0.488, 0.3455, 0.005, 0.2055, 0.3315, 0.0075, 0.292, 0.0835, 0.0245, 0.276, 0.1515, 0.1985, 0.2865, 0.2415, 0.2845, 0.479, 0.321, 0.042, 0.0875, 0.2405, 0.0995, 0.1955, 0.7735, 0.564, `3516` = 12L, `3517` = 11L, `3518` = 11L, `3519` = 11L, `3520` = 10L, `3521` = 11L, `3524` = 4L, `3525` = 6L, `3526` = 6L, `3527` = 6L, `3529` = 6L, `3531` = 7L, `3532` = 7L, `3533` = 7L, `3534` = 6L, `3535` = 6L, `3536` = 9L, `3537` = 8L, `3538` = 6L, `3540` = 8L, `3541` = 8L, `3543` = 8L, `3544` = 8L, `3545` = 9L, `3547` = 7L, `3548` = 9L, `3550` = 9L, `3551` = 8L, `3552` = 9L, `3555` = 8L, `3556` = 8L, `3557` = 9L, `3558` = 8L, `3559` = 10L, `3560` = 10L, `3561` = 10L, `3562` = 9L, `3564` = 8L, `3565` = 9L, `3566` = 9L, `3567` = 9L, `3568` = 10L, `3570` = 11L, `3571` = 9L, `3572` = 9L, `3573` = 9L, `3574` = 10L, `3576` = 8L, `3578` = 11L, `3579` = 10L, `3580` = 9L, `3581` = 10L, `3582` = 9L, `3583` = 10L, `3584` = 9L, `3585` = 11L, `3586` = 9L, `3587` = 11L, `3589` = 12L, `3590` = 11L, `3591` = 11L, `3592` = 9L, `3593` = 11L, `3594` = 8L, `3595` = 12L, `3596` = 11L, `3597` = 10L, `3598` = 11L, `3599` = 12L, `3600` = 12L, `3601` = 4L, `3604` = 7L, `3606` = 8L, `3607` = 7L, `3609` = 7L, 0.312, 0.263, 0.186, 0.0745, 0.3075, 0.2595, 0.391, 0.0205, 0.202, 0.2855, 0.2355, 0.2115, 0.255, 0.296, 0.271, 0.4935, 0.8225, 0.769, 1.133, 1.0745, 0.059, 0.0445, 0.0575, 0.0625, 0.0935, 0.1105, 0.199, 0.1925, 0.163, 0.2205, 0.251, 0.282, 0.416, 0.3545, 0.53, 0.6135, 0.6865, 0.758, 0.6705, 0.011, 0.04, 0.0575, 0.098, 0.115, 0.181, 0.352, 0.2565, 0.3525, 0.4315, 0.503, 0.5515, 0.62, 0.5425, 0.4865, 0.665, 0.61, 0.7055, 0.713, 0.708, 0.037, 0.0865, 0.1005, 0.1125, 0.174, 0.258, 0.3165, 0.2075, 0.327, 0.242, 0.3595, 0.322, 0.447, 0.4815, 0.4025, 0.3615, 0.445, 0.285, 0.6335, 0.46, 0.5805, 0.675, 0.5755, 0.797, 0.302, 0.327, 0.375, 0.553, 0.653, 0.093, 0.153, 0.1335, 0.1, 0.175, 0.246, 0.1875, 0.2365, 0.278, 0.3795, 0.348, 0.4405, 0.343, 0.3395, 0.581, 0.516, 0.506, 0.639, 0.4865, 0.423, 0.488, 0.5995, 0.645, 0.5175, 0.467, 0.594, 0.577, 0.728, 0.7365, 0.475, 0.7075, 0.8825, 0.8725, 0.844, 0.7755, 0.2365, 0.2405, 0.569, 0.3495, 0.531, 0.6575, 0.6185, 0.586, 0.048, 0.073, 0.215, 0.1755, 0.197, 0.3195, 0.321, 0.378, 0.331, 0.3695, 0.4405, 0.52, 0.535, 0.5205, 0.468, 0.5725, 0.624, 0.642, 0.6205, 0.664, 0.8005, 0.063, 0.0565, 0.0835, 0.1885, 0.219, 0.1575, 0.2185, 0.241, 0.201, 0.35, 0.458, 0.4255, 0.181, 0.1255, 0.15, 0.232, 0.2865, 0.3155, 0.4, 0.37, 0.5255, 0.531, 0.9455), VisceraWeight = c(0.101, 0.0485, 0.1415, 0.114, 0.0395, 0.0775, 0.1415, 0.1495, 0.1125, 0.1475, 0.081, 0.095, 0.0805, 0.133, 0.0395, 0.087, 0.043, 0.075, 0.062, 0.049, 0.214, 0.21, 0.301, 0.188, 0.272, 0.219, 0.227, 0.242, 0.3575, 0.3925, 0.124, 0.3075, 0.1165, 0.2035, 0.086, 0.091, 0.0235, 0.015, 0.0125, 0.045, 0.227, 0.11, 0.0255, 0.111, 0.06, 0.096, 0.1055, 0.0915, 0.1755, 0.0955, 0.12, 0.014, 0.13, 0.1125, 0.16, 0.1935, 0.1315, 0.1015, `3611` = 9L, `3612` = 10L, `3613` = 10L, `3614` = 11L, `3615` = 11L, `3617` = 14L, `3620` = 12L, `3621` = 9L, `3622` = 8L, `3623` = 10L, `3624` = 10L, `3626` = 10L, `3627` = 10L, `3628` = 10L, `3630` = 6L, `3631` = 7L, `3632` = 5L, `3633` = 5L, `3634` = 5L, `3635` = 6L, `3637` = 8L, `3638` = 6L, `3639` = 8L, `3640` = 8L, `3641` = 7L, `3642` = 9L, `3643` = 8L, `3644` = 8L, `3645` = 7L, `3646` = 9L, `3647` = 9L, `3648` = 8L, `3650` = 9L, `3652` = 10L, `3654` = 9L, `3655` = 9L, `3656` = 9L, `3657` = 11L, `3658` = 7L, `3660` = 9L, `3661` = 10L, `3662` = 10L, `3663` = 8L, `3664` = 9L, `3665` = 9L, `3668` = 11L, `3669` = 10L, `3670` = 9L, `3672` = 10L, `3673` = 10L, `3674` = 10L, `3675` = 12L, `3678` = 11L, `3679` = 10L, `3680` = 10L, `3681` = 9L, `3682` = 11L, `3683` = 11L, `3685` = 11L, `3686` = 10L, `3689` = 11L, `3692` = 12L, `3694` = 11L, `3696` = 10L, `3697` = 13L, `3698` = 12L, `3699` = 10L, `3700` = 11L, `3701` = 10L, `3703` = 11L, `3704` = 11L, `3705` = 11L, `3706` = 9L, `3707` = 11L, `3708` = 11L, `3709` = 9L, `3710` = 10L, `3711` = 11L, `3712` = 11L, `3713` = 10L, `3716` = 11L, `3717` = 5L, `3718` = 7L, `3720` = 7L, `3724` = 9L, `3725` = 7L, `3726` = 9L, `3727` = 8L, `3728` = 7L, `3729` = 10L, 0.1155, 0.0405, 0.168, 0.0985, 0.225, 0.261, 0.2895, 0.221, 0.189, 0.2355, 0.252, 0.192, 0.225, 0.216, 0.2225, 0.205, 0.2075, 0.114, 0.231, 0.1675, 0.1525, 0.254, 0.318, 0.3425, 0.388, 0.132, 0.133, 0.085, 0.046, 0.1915, 0.1835, 0.183, 0.1315, 0.1345, 0.107, 0.0635, 0.063, 0.1585, 0.102, 0.0955, 0.172, 0.0595, 0.1035, 0.1725, 0.051, 0.0505, 0.0235, 0.0545, 0.061, 0.2635, 0.261, 0.283, 0.063, 0.036, 0.056, 0.0345, 0.063, 0.0575, 0.2055, 0.1435, 0.1165, 0.1075, 0.108, 0.039, 0.0065, 0.008, 0.262, 0.2585, 0.305, 0.2355, 0.1495, 0.131, 0.2615, 0.2135, 0.1785, 0.2475, 0.2575, 0.2895, 0.483, 0.4515, `3730` = 9L, `3731` = 9L, `3732` = 9L, `3733` = 10L, `3734` = 10L, `3735` = 10L, `3736` = 9L, `3738` = 9L, `3739` = 9L, `3741` = 11L, `3742` = 12L, `3743` = 13L, `3745` = 5L, `3748` = 6L, `3750` = 7L, `3751` = 8L, `3752` = 7L, `3754` = 8L, `3755` = 8L, `3756` = 8L, `3758` = 10L, `3760` = 9L, `3761` = 9L, `3762` = 10L, `3763` = 8L, 0.3085, 0.409, 0.541, 0.169, 0.1845, 0.0475, 0.015, 0.2165, 0.2, 0.266, 0.268, 0.2965, 0.3315, 0.335, 0.2165, 0.1815, 0.06, 0.1965, 0.1535, 0.2635, 0.095, 0.205, 0.155, 0.071, 0.1085, 0.085, 0.1685, 0.1625, 0.0415, 0.1635, 0.111, 0.07, 0.115, 0.0755, 0.1125, 0.1235, 0.144, 0.125, 0.1055, 0.082, 0.0485, 0.2095, 0.158, 0.193, 0.287, 0.029, 0.083, 0.032, 5e-04, 0.003, 0.0045, 0.0245, 0.0105, 0.0265, 0.039, 0.0345, 0.0325, 0.0415, 0.038, 0.035, 0.2805, 0.2165, 0.217, 0.2065, 0.175, 0.2555, 0.1725, 0.239, 0.0245, 0.028, 0.1525, 0.186, `3764` = 11L, `3765` = 10L, `3767` = 10L, `3768` = 10L, `3769` = 9L, `3771` = 9L, `3773` = 9L, `3774` = 9L, `3776` = 8L, `3777` = 13L, `3779` = 9L, `3780` = 10L, `3781` = 10L, `3782` = 10L, `3785` = 11L, `3787` = 10L, `3788` = 12L, `3789` = 8L, `3791` = 10L, `3792` = 11L, `3793` = 13L, `3794` = 14L, `3795` = 10L, `3796` = 12L, `3797` = 11L, `3798` = 13L, `3800` = 11L, `3802` = 3L, `3803` = 6L, `3804` = 7L, `3805` = 7L, `3806` = 9L, `3807` = 8L, `3808` = 8L, `3809` = 10L, `3810` = 8L, `3812` = 9L, `3816` = 8L, `3817` = 8L, `3818` = 8L, `3819` = 9L, `3822` = 10L, `3823` = 9L, `3825` = 7L, `3826` = 11L, `3827` = 10L, `3828` = 11L, `3830` = 12L, `3832` = 10L, `3833` = 14L, `3835` = 6L, `3836` = 7L, `3837` = 9L, `3838` = 4L, `3839` = 8L, `3840` = 8L, `3841` = 9L, `3842` = 11L, `3843` = 9L, `3844` = 16L, `3845` = 14L, `3847` = 6L, `3848` = 9L, `3849` = 14L, `3851` = 7L, `3852` = 10L, `3854` = 9L, `3855` = 11L, `3857` = 9L, `3858` = 16L, `3860` = 9L, `3861` = 14L, `3862` = 14L, `3863` = 10L, `3864` = 18L, `3865` = 5L, `3866` = 19L, `3867` = 10L, `3868` = 15L, `3870` = 9L, `3871` = 15L, `3873` = 9L, `3874` = 5L, `3877` = 16L, `3878` = 15L, `3879` = 10L, `3880` = 15L, `3882` = 10L, `3884` = 10L, `3886` = 7L, `3890` = 12L, `3891` = 16L, `3892` = 12L, `3893` = 13L, `3894` = 9L, `3895` = 12L, `3896` = 13L, `3897` = 14L, `3898` = 17L, `3899` = 10L, `3900` = 4L, `3901` = 15L, `3902` = 12L, `3903` = 4L, `3905` = 16L, `3906` = 6L, `3907` = 4L, `3908` = 13L, `3909` = 9L, `3910` = 10L, 0.0635, 0.0885, 0.2705, 0.197, 0.327, 0.245, 0.3345, 0.3115, 0.1605, 0.1585, 0.057, 0.104, 0.127, 0.158, 0.1965, 0.0815, `3913` = 15L, `3915` = 19L, `3917` = 10L, `3919` = 18L, `3920` = 11L, `3921` = 8L, `3922` = 10L, `3923` = 10L, `3924` = 12L, `3926` = 8L, `3929` = 10L, `3930` = 16L, `3932` = 17L, `3936` = 11L, `3937` = 13L, `3939` = 10L, `3940` = 14L, `3941` = 11L, `3942` = 15L, `3946` = 6L, `3948` = 16L, `3949` = 12L, `3950` = 13L, `3951` = 13L, `3954` = 8L, `3955` = 7L, `3956` = 9L, `3958` = 10L, `3960` = 11L, `3961` = 11L, `3962` = 10L, `3963` = 10L, `3964` = 4L, `3965` = 5L, `3966` = 6L, 0.1795, 0.2205, 0.19, 0.034, 0.0295, 0.1275, 0.0895, 0.174, 0.0595, 0.0495, 0.114, 0.0095, 0.2355, 0.3055, 0.2045, 0.3235, 0.27, 0.2945, 0.067, 0.1015, 0.1385, 0.0255, 0.022, 0.0065, 0.056, 0.053, 0.0415, 0.1665, 0.04, 0.0565, 0.042, 0.015, 0.4085, 0.3175, 0.2655, 0.276, 0.325, 0.3065, 0.1495, 0.152, 0.2185, 0.0555, 0.2455, 0.2905, 0.1635, 0.2065, 0.259, 0.49, 0.336, 0.287, 0.2995, 0.2705, 0.244, 0.1655, 0.2735, 0.2765, 0.299, 0.3265, 0.453, 0.314, 0.292, 0.3695, 0.3365, 0.2875, 0.183, 0.3135, 0.189, 0.137, 0.122, 0.21, 0.1245, 0.152, 0.1225, 0.1435, 0.119, 0.137, 0.0545, 0.0535, 0.064, 0.176, 0.144, 0.2075, 0.131, 0.152, 0.083, 0.1045, 0.182, 0.246, 0.2425, 0.2115, 0.2385, 0.1715, 0.2465, 0.224, 0.267, 0.349, `3968` = 6L, `3971` = 6L, `3972` = 6L, `3973` = 8L, `3974` = 7L, `3976` = 6L, `3977` = 7L, `3979` = 8L, `3980` = 6L, `3981` = 7L, 0.16, 0.358, 0.139, 0.123, 0.0235, 0.203, 0.2435, 0.1735, 0.129, 0.1665, 0.196, 0.156, 0.13, 0.0905, 0.137, 0.055, 0.138, 0.0525, 0.1625, 0.0325, 0.232, 0.159, 0.4725, 0.354, 0.2195, 0.1735, 0.259, 0.045, 0.057, 0.1525, 0.061, 0.018, 0.012, 0.02, 0.3035, 0.2735, 0.305, 0.2665, 0.1715, 0.1405, `3984` = 6L, `3986` = 11L, `3987` = 10L, `3988` = 11L, `3990` = 11L, `3991` = 10L, `3995` = 4L, `3996` = 5L, `3997` = 6L, `3998` = 6L, `3999` = 6L, `4000` = 6L, `4002` = 6L, `4005` = 10L, `4006` = 9L, `4007` = 9L, `4008` = 11L, `4009` = 12L, `4011` = 9L, `4014` = 8L, `4015` = 10L, `4016` = 10L, `4019` = 8L, `4020` = 10L, `4021` = 11L, `4022` = 10L, `4024` = 6L, `4027` = 7L, `4028` = 6L, `4029` = 8L, `4031` = 7L, `4032` = 8L, `4033` = 8L, `4034` = 10L, `4035` = 7L, `4036` = 11L, `4037` = 8L, `4038` = 10L, `4041` = 12L, `4043` = 10L, `4044` = 8L, `4045` = 10L, `4046` = 9L, `4047` = 11L, `4049` = 13L, `4050` = 9L, `4051` = 9L, `4053` = 13L, `4057` = 11L, `4058` = 11L, 0.0645, 0.1455, 0.1395, 0.091, 0.254, 0.3405, 0.2635, 0.3215, 0.2445, 0.1765, 0.212, 0.237, 0.1615, 0.3065, 0.1795, 0.2195, 0.19, 0.181, 0.2635, 0.2185, 0.2235, 0.2595, 0.2315, 0.1635, 0.256, 0.2305, 0.247, 0.157, 0.221, 0.204, 0.209, 0.245, 0.1265, 0.181, 0.0365, 0.0195, 0.0575, 0.0215, 0.034, 0.0085, 0.0445, 0.008, 0.0095, 0.003, 0.159, 0.156, 0.1055, 0.1055, 0.1225, 0.08, 0.145, 0.0255, 0.066, 0.1415, 0.0925, 0.057, 0.0695, 0.051, 0.038, 0.018, 0.2005, 0.203, 0.195, 0.1735, 0.131, 0.141, 0.1665, 0.205, 0.298, 0.1055, 0.0805, 0.092, 0.163, 0.126, 0.0385, 0.1055, 0.1005, 0.109, 0.0875, 0.2275, 0.201, 0.2, 0.2355, 0.1975, 0.2495, 0.3035, 0.171, 0.3435, 0.065, 0.174, 0.1905, 0.076, 0.058, 0.1675, 0.057, 0.207, 0.153, 0.1825, 0.201, 0.2245, 0.1965, 0.0635, 0.06, 0.108, 0.0795, 0.0455, 0.0675, 0.042, 0.0355, 0.0705, 0.1, 0.07, 0.095, 0.047, 0.0335, 0.0105, 0.0425, 0.13, 0.138, 0.1095, 0.116, 0.1125, 0.061, 0.0525, 0.185, 0.1235, 0.095, 0.0945, 0.0525, 0.041, 0.054, 0.0485, 0.108, 0.1365, 0.2655, 0.156, 0.045, 0.0795, 0.13, 0.105, 0.081, 0.014, 0.04, 0.024, 0.0465, 0.2315, 0.2245, 0.254, 0.1615, 0.3445, 0.1555, 0.0665, 0.054, 0.144, 0.14, 0.243, 0.123, 0.108, 0.169, 0.124, 0.14, 0.148, 0.1915, 0.0465, 0.1415, 0.094, 0.1725, 0.1665, 0.2325, 0.1425, 0.2055, 0.2425, 0.0205, 0.0655, 0.0025, 0.04, 0.0285, 0.1145, 0.091, 0.0685, 0.142, 0.09, 0.1015, 0.037, 0.057, 0.0355, 0.0475, 0.053, 0.0385, 0.0205, 0.019, 0.0115, 0.0055, 0.004, 0.0055, 0.228, 0.212, 0.243, 0.125, 0.0705, 0.1195, 0.159, 0.2015, 0.1825, 0.176, 0.1835, 0.172, 0.1735, 0.1525, 0.074, 0.2175, 0.183, 0.1945, 0.226, 0.4805, 0.2035, 0.1615, 0.3045, 0.272, 0.236, 0.2315, 0.167, 0.1825, 0.221, 0.361, 0.296, 0.227, 0.2395, 0.2815, 0.123, 0.158, 0.167, 0.17, 0.179, 0.142, 0.1505, 0.1375, 0.152, 0.206, 0.0165, 0.2325, 0.13, 0.1375, 0.2025, 0.246, 0.2575, 0.1315, 0.225, 0.26, 0.1635, 0.1685, 0.096, 0.157, 0.081, 0.117, 0.1055, 0.0935, 0.045, 0.2095, 0.179, 0.179, 0.174, 0.0125, 0.0345, 0.0345, 0.04, 0.0575, 0.048, 0.0445, 0.0455, 0.0585, 0.0635, 0.084, 0.0655, 0.0755, 0.0505, 0.0645, 0.084, 0.086, 0.1, 0.0935, 0.113, 0.096, 0.096, 0.129, 0.1275, 0.1425, 0.1915, 0.1725, 0.2015, 0.2015, 0.1755, 0.1755, 0.1815, 0.244, 0.296, 0.244, 0.2875, 0.1695, 0.276, 0.3015, 0.33, 0.2605, 0.3315, 0.2815, 0.275, 0.2545, 0.162, 0.285, 0.2755, 0.305, 0.2605, 0.332, 0.315, 0.291, 0.3415, 0.212, 0.2945, 0.391, 0.3405, 0.322, 0.3775, 0.422, 0.419, 0.014, 0.0135, 0.0215, 0.0215, 0.029, 0.05, 0.0265, 0.031, 0.0305, 0.0265, 0.044, 0.038, 0.0455, 0.0425, 0.0535, 0.058, 0.0695, 0.061, 0.092, 0.0795, 0.096, 0.072, 0.087, 0.061, 0.078, 0.0835, 0.1095, 0.0945, 0.127, 0.093, 0.0855, 0.153, 0.1055, 0.093, 0.0925, 0.106, 0.092, 0.1215, 0.1065, 0.1405, 0.099, 0.117, 0.0955, 0.124, 0.1295, 0.1435, 0.1535, 0.103, 0.129, 0.12, 0.141, 0.131, 0.133, 0.1205, 0.1455, 0.1225, 0.134, 0.1815, 0.137, 0.173, 0.197, 0.232, 0.196, 0.1595, 0.178, 0.207, 0.1665, 0.21, 0.255, 0.2195, 0.1935, 0.2745, 0.2115, 0.22, 0.2425, 0.2445, 0.178, 0.2205, 0.2435, 0.2095, 0.2645, 0.258, 0.239, 0.2595, 0.276, 0.278, 0.271, 0.2475, 0.3035, 0.302, 0.275, 0.284, 0.248, 0.2705, 0.3, 0.326, 0.236, 0.342, 0.3555, 0.299, 0.327, 0.2055, 0.312, 0.308, 0.3225, 0.3025, 0.297, 0.3595, 0.294, 0.3145, 0.2995, `4060` = 8L, `4061` = 8L, `4062` = 10L, `4064` = 11L, `4065` = 9L, `4066` = 6L, `4067` = 7L, `4068` = 6L, `4069` = 7L, `4070` = 8L, `4071` = 7L, `4073` = 8L, `4074` = 9L, `4075` = 8L, `4076` = 8L, `4078` = 9L, `4079` = 10L, `4080` = 8L, `4081` = 8L, `4083` = 9L, `4084` = 10L, `4085` = 10L, `4086` = 11L, `4088` = 10L, `4089` = 9L, `4090` = 9L, `4091` = 11L, `4092` = 12L, `4094` = 11L, `4096` = 11L, `4098` = 9L, `4099` = 9L, `4100` = 9L, `4101` = 9L, `4102` = 11L, 0.3615, 0.3215, 0.362, 0.396, 0.42, 0.5225, 0.4185, 0.383, 0.44, 0.509, 0.005, 0.009, 0.0125, 0.013, 0.0155, 0.0175, 0.02, 0.0205, 0.0265, 0.027, 0.0295, 0.0475, 0.0265, 0.042, 0.0495, 0.053, 0.044, 0.072, 0.07, 0.051, 0.0865, 0.0865, 0.0735, 0.084, 0.0905, 0.1075, 0.1, 0.098, 0.1395, 0.1075, 0.099, 0.111, 0.1085, 0.13, 0.116, 0.1135, 0.117, 0.15, 0.1395, 0.115, 0.1195, 0.1135, 0.1265, 0.1845, 0.1295, 0.1985, 0.1285, 0.1395, 0.127, 0.138, 0.1815, 0.1405, 0.127, 0.1855, 0.195, 0.1765, 0.1775, 0.2175, 0.1765, 0.1885, 0.2395, 0.2375, 0.217, 0.214, 0.197, 0.197, 0.2175, 0.1945, 0.2155, 0.426, 0.1915, 0.1815, 0.232, 0.2495, 0.2395, 0.262, 0.257, 0.2365, 0.2195, 0.212, 0.2255, 0.242, 0.272, 0.1825, 0.25, 0.2005, 0.1525, `4103` = 11L, `4104` = 10L, `4105` = 11L, `4106` = 9L, `4107` = 11L, `4109` = 7L, `4110` = 8L, `4111` = 9L, `4112` = 8L, `4115` = 9L, `4117` = 9L, `4118` = 9L, `4119` = 9L, `4120` = 4L, `4121` = 7L, `4122` = 9L, `4123` = 8L, `4125` = 8L, `4126` = 9L, `4127` = 11L, `4128` = 10L, `4130` = 10L, `4133` = 10L, `4136` = 11L, `4137` = 9L, `4139` = 11L, `4140` = 10L, `4141` = 10L, `4142` = 11L, `4143` = 13L, `4144` = 13L, `4146` = 11L, `4147` = 10L, `4149` = 11L, `4150` = 6L, `4151` = 7L, `4152` = 6L, `4154` = 8L, `4155` = 6L, `4156` = 6L, `4157` = 8L, `4158` = 8L, `4159` = 8L, `4160` = 9L, `4161` = 11L, `4162` = 11L, `4164` = 7L, `4165` = 7L, `4166` = 7L, `4167` = 10L, `4169` = 8L, `4171` = 10L, `4172` = 8L, `4173` = 11L, `4175` = 9L, `4176` = 10L, `4177` = 12L), weights = NULL, start = NULL, etastart = NULL, 0.2445, 0.2715, 0.29, 0.211, 0.3065, 0.308, 0.3415, 0.2945, 0.254, 0.339, 0.3135, 0.369, 0.3725, 0.279, 0.291, 0.3025, 0.3655, 0.3685, 0.3105, 0.405, 0.3485, 0.3715, 0.418, 0.236, 0.381, 0.407, 0.313, 0.469, 0.1945, 0.4255, 0.55, 0.4585, 0.5195, 0.513, 0.4015, 0.4985, 0.1045, 0.014, 0.014, 0.015, 0.0295, 0.0205, 0.015, 0.031, 0.0235, 0.032, 0.0395, 0.032, mustart = NULL, offset = NULL, family = list(family = "gaussian", link = "identity", linkfun = function (mu) mu, linkinv = function (eta) eta, variance = function (mu) rep.int(1, length(mu)), dev.resids = function (y, mu, wt) wt * ((y - mu)^2), aic = function (y, n, mu, wt, dev) { nobs <- length(y) nobs * (log(dev/nobs * 2 * pi) + 1) + 2 - sum(log(wt)) }, mu.eta = function (eta) rep.int(1, length(eta)), initialize = expression({ n <- rep.int(1, nobs) if (is.null(etastart) && is.null(start) && is.null(mustart) && ((family$link == "inverse" && any(y == 0)) || (family$link == "log" && any(y <= 0)))) stop("cannot find valid starting values: please specify some") mustart <- y }), validmu = function (mu) TRUE, valideta = function (eta) TRUE, dispersion = NA_real_), control = list(epsilon = 1e-08, maxit = 25, trace = FALSE), intercept = TRUE, singular.ok = TRUE)
0.0405, 0.033, 0.037, 0.037, 0.036, 0.0305, 0.035, 0.042, 0.0375, 0.049, 0.0655, 0.041, 0.045, 0.0405, 0.0415, 0.048, 0.039, 0.0535, 0.042, 0.0595, 0.038, 0.042, 0.0575, 0.0595, 2: eval(call(if (is.function(method)) "method" else method, x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0L, singular.ok = singular.ok))
3: eval(call(if (is.function(method)) "method" else method, x = X, y = Y, weights = weights, start = start, etastart = etastart, 0.061, 0.0605, 0.0505, 0.0705, 0.0615, 0.045, 0.06, 0.086, 0.0715, 0.083, 0.089, 0.0755, 0.108, 0.0775, 0.099, 0.077, 0.1045, 0.073, 0.0855, 0.105, 0.0885, 0.091, 0.0885, 0.11, 0.101, 0.1045, 0.116, 0.1375, 0.1415, 0.1375, 0.124, 0.1205, 0.1435, 0.103, 0.1295, 0.1255, 0.1365, 0.131, 0.111, 0.1235, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0L, singular.ok = singular.ok))
4: stats::glm(formula = formula, data = data, family = stats::gaussian)
5: withVisible(...elt(i))
6: utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 0.135, 0.144, 0.163, 0.1445, 0.163, 0.1185, 0.171, 0.137, 0.1905, 0.1555, 0.1545, 0.156, 0.1435, 0.1765, 0.185, 0.1305, 0.3225, 0.1615, 0.21, 0.1685, 0.184, 0.13, 0.1415, 0.1785, 0.1835, 0.147, 0.2095, 0.176, 0.273, 0.167, 0.156, 0.1965, 0.2535, 0.234, 0.2345, 0.229, 0.199, 0.1855, 0.188, 0.196, summary.fixed <- list(mean = stats::coefficients(out))}, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) 0.112, 0.272, 0.1885, 0.2465, 0.25, 0.1715, 0.189, 0.226, 0.206, 0.174, 0.2215, 0.1835, 0.2335, 0.1725, 0.29, 0.197, 0.193, 0.228, 0.1955, 0.302, 0.2385, 0.254, 0.3135, 0.2685, })
7: tryCatchList(expr, classes, parentenv, handlers)
0.2345, 0.256, 0.326, 0.239, 0.345, 0.295, 0.1785, 0.1865, 0.2645, 0.2725, 0.2695, 0.2255, 0.372, 0.2995, 0.2385, 0.3515, 0.245, 0.274, 0.3585, 0.349, 0.3215, 0.3595, 0.3865, 0.294, 0.3525, 0.3125, 0.342, 0.271, 0.3135, 0.416, 0.399, 0.4865, 0.383, 0.4195, 0.345, 0.389, 0.564, 0.3585, 0.4345, 0.46, 0.446, 0.519, 0.42, 0.047, 0.041, 0.046, 0.0665, 0.0455, 0.061, 0.075, 0.061, 0.063, 0.0905, 0.0745, 0.08, 0.092, 8: tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) 0.089, 0.096, 0.1105, 0.1165, 0.093, 0.117, 0.1025, 0.1075, 0.103, 0.1285, 0.1245, 0.1565, 0.096, 0.181, 0.139, 0.145, 0.1415, 0.151, 0.1775, 0.218, 0.2035, 0.161, 0.1835, 0.207, 0.2065, 0.2205, 0.2165, 0.23, 0.215, 0.239, 0.2205, 0.2655, 0.2085, 0.193, 0.2875, 0.229, 0.1915, 0.235, 0.3245, 0.26, mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out))}, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) 0.268, 0.2745, 0.2425, 0.2655, 0.318, 0.3345, 0.365, 0.3585, 0.326, 0.304, 0.2435, 0.296, 0.4095, 0.351, 0.324, 0.4475, 0.3255, 0.3325, 0.3265, 0.4095, 0.363, 0.413, 0.427, 0.397, 0.017, 0.0155, 0.034, 0.0495, 0.056, 0.0365, 0.046, 0.0925, 0.0665, 0.0395, 0.0505, 0.053, 0.0575, 0.048, 0.0585, 0.07, 0.085, 0.083, 0.081, 0.0815, 0.137, 0.099, 0.106, 0.1285, 0.0945, 0.1285, 0.091, 0.1265, 0.147, 0.135, 0.143, 0.131, 0.123, 0.14, 0.1265, 0.1595, 0.1635, 0.159, 0.1605, 0.1455, 0.1305, 0.1395, 0.1725, 0.255, 0.1175, 0.169, 0.1545, 0.1455, 0.1155, 0.112, 0.1345, 0.16, 0.176, 0.15, 0.158, 0.187, 0.149, 0.221, 0.186, 0.1825, 0.1735, 0.185, 0.148, 0.144, 0.1705, 0.196, 0.238, 0.188, 0.2175, 0.1465, 0.218, 0.198, 0.166, 0.1515, 0.2225, 0.1405, 0.2205, 0.235, 0.268, 0.1785, 0.17, 0.2065, 0.1925, 0.196, 0.1965, 0.2225, 0.2455, 0.241, 0.3045, 0.1775, 0.2265, 0.2345, 0.224, 0.264, 0.228, 0.2515, 0.2915, }))
9: (function (formula, data, r = 1/1000, logn = log(1000), relat = c("cos", "sigmoid", "tanh", "atan", "sin", "erf")) { fparam <- NULL fmla.proc <- as.character(formula)[2:3] fobserved <- fmla.proc[1] fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = " ", replacement = "") fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = "\n", replacement = "") fparam <- stringi::stri_split_fixed(str = fmla.proc[2], pattern = "+I", 0.2345, 0.223, 0.2405, 0.261, 0.256, 0.2435, 0.246, 0.2695, 0.2265, 0.2575, 0.2225, 0.2615, 0.22, 0.2055, 0.2615, 0.2865, 0.2325, 0.273, 0.2315, 0.271, 0.3445, 0.309, 0.313, 0.2665, omit_empty = FALSE)[[1]] sj <- (stringi::stri_count_fixed(str = fparam, pattern = "*")) sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = "+")) 0.2695, 0.2925, 0.4205, 0.2615, 0.288, 0.3715, 0.345, 0.309, 0.308, 0.2735, 0.3195, 0.337, 0.2905, 0.281, 0.223, 0.3805, 0.269, 0.3125, 0.3295, 0.3545, 0.3845, 0.253, 0.2925, 0.32, 0.314, 0.311, 0.2865, 0.3095, 0.3305, 0.2755, 0.391, 0.339, 0.293, 0.375, 0.3675, 0.3935, 0.48, 0.4475, 0.4925, 0.454, 0.4425, 0.3215, 0.487, 0.5195, 0.5185, 0.4825, 0.6415, 0.76, 0.057, 0.131, 0.139, 0.09, 0.0865, 0.1715, 0.0855, 0.1535, 0.1395, 0.2455, 0.185, 0.215, 0.223, 0.198, 0.2495, 0.155, 0.2295, 0.318, 0.311, 0.2245, 0.1715, 0.2685, 0.2035, 0.2205, 0.3055, 0.232, 0.2715, 0.2535, 0.3115, 0.307, 0.2295, 0.326, 0.3685, 0.333, 0.293, 0.33, 0.4015, 0.4265, 0.375, 0.409, 0.013, 0.0275, 0.0335, 0.0325, 0.0445, 0.0525, 0.067, 0.0345, 0.064, 0.0615, 0.0625, 0.0625, 0.0665, 0.0945, 0.056, 0.067, for (rel in relat) { sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = rel)) } tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out)) }, error = function(err) { print(err) 0.099, 0.08, 0.178, 0.1025, 0.0965, 0.1165, 0.0975, 0.106, 0.1175, 0.1425, 0.152, 0.1335, 0.0915, 0.1335, 0.146, 0.17, 0.156, 0.12, 0.1535, 0.159, 0.0965, 0.1275, 0.115, 0.148, 0.1765, 0.231, 0.1405, 0.1905, 0.1465, 0.1585, 0.1755, 0.171, 0.179, 0.1785, 0.158, 0.205, 0.1955, 0.1525, 0.193, 0.2145, 0.2225, 0.2085, 0.1615, 0.162, 0.2115, 0.1925, 0.173, 0.173, 0.185, 0.2185, 0.1645, 0.201, 0.1955, 0.337, 0.2195, 0.2885, 0.1935, 0.1985, 0.2565, 0.2615, 0.1975, 0.2825, 0.283, 0.3195, 0.227, 0.302, 0.213, 0.346, 0.2315, 0.212, 0.2405, 0.2965, 0.351, 0.2, 0.246, 0.3065, 0.296, 0.314, 0.246, 0.213, 0.2975, 0.3, 0.2555, 0.2505, 0.269, 0.322, 0.2375, 0.3655, 0.296, 0.278, 0.384, 0.2605, 0.223, 0.2775, 0.303, 0.2845, 0.3025, 0.354, 0.379, 0.402, 0.3835, 0.2985, 0.347, 0.3835, 0.3605, 0.366, 0.4915, 0.0065, 0.005, 0.012, 0.0265, 0.0185, 0.023, 0.0205, 0.0235, 0.022, 0.0255, 0.0355, 0.044, 0.0465, 0.0335, 0.0525, 0.034, 0.047, 0.05, 0.068, 0.063, 0.102, 0.084, 0.082, mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) })) return(list(mlik = mlik, waic = waic, dic = dic, summary.fixed = summary.fixed)) 0.1525, 0.0865, 0.1095, 0.1385, 0.1315, 0.154, 0.1855, 0.1565, 0.1905, 0.2435, 0.212, 0.156, 0.2705, 0.273, 0.177, 0.2085, 0.228, 0.252, 0.353, 0.0185, 0.0225, 0.022, 0.0225, 0.033, 0.0305, 0.076, 0.065, 0.091, 0.0825, 0.1045, 0.116, 0.1175, 0.0885, 0.085, 0.074, 0.1315, 0.1345, 0.1575, 0.143, 0.149, })(data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 7L, 6L, 8L, 7L, 10L, 10L, 7L, 8L, 8L, 8L, 4L, 7L, 7L, 9L, 10L, 8L, 8L, 10L, 6L, 13L, 8L, 20L, 11L, 13L, 15L, 9L, 14L, 9L, 12L, 16L, 14L, 12L, 13L, 10L, 9L, 12L, 15L, 12L, 13L, 10L, 15L, 14L, 8L, 7L, 10L, 7L, 15L, 12L, 11L, 10L, 9L, 9L, 9L, 9L, 11L, 11L, 10L, 9L, 8L, 9L, 14L, 6L, 6L, 0.1895, 0.1565, 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0.34, 0.335, 0.33, 0.275, 0.38, 0.4, 0.41, 0.405, 0.175, 0.24, 0.25, 0.25, 0.27, 0.25, 0.275, 0.275, 0.275, 0.29, 0.29, 0.325, 0.32, 0.305, 0.325, 0.365, 0.335, 0.325, 0.375, 0.365, 0.355, 0.385, 0.41, 0.4, 0.395, 0.405, 0.42, 0.44, 0.45, 0.45, 0.435, 0.465, 0.475, 0.48, 0.465, 0.475, 0.475, 0.495, 0.455, 0.5, 0.52, 0.51, 0.505, 0.505, 0.5, 0.49, 0.49, 0.505, 0.51, 0.485, 0.51, 0.525, 0.52, 0.58, 0.53, 0.585, 0.525, 0.525, 0.56, 0.54, 0.56, 0.595, 0.175, 0.175, 0.19, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0.18, 0.12, 0.23, 0.235, 0.22, 0.23, 0.24, 0.25, 0.25, 0.25, 0.25, 0.265, 0.29, 0.31, 0.31, 0.32, 0.315, 0.34, 0.315, 0.325, 0.325, 0.34, 0.345, 0.335, 0.35, 0.36, 0.355, 0.345, 0.335, 0.375, 0.36, 0.365, 0.35, 0.365, 0.37, 0.375, 0.385, 0.36, 0.365, 0.37, 0.385, 0.395, 0.4, 0.39, 0.385, 0.385, 0.4, 0.39, 0.39, 0.41, 0.355, 0.39, 0.375, 0.405, 0.41, 0.39, 0.415, 0.425, 0.425, 0.4, 0.43, 0.445, 0.455, 0.4, 0.45, 0.455, 0.445, 0.46, 0.475, 0.455, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0.45, 0.47, 0.455, 0.47, 0.45, 0.475, 0.46, 0.49, 0.47, 0.475, 0.48, 0.51, 0.48, 0.49, 0.475, 0.5, 0.485, 0.485, 0.495, 0.48, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0.49, 0.525, 0.5, 0.5, 0.52, 0.52, 0.5, 0.5, 0.515, 0.495, 0.52, 0.525, 0.505, 0.535, 0.495, 0.475, 0.525, 0.57, 0.565, 0.535, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0.525, 0.535, 0.535, 0.565, 0.565, 0.525, 0.6, 0.6, 0.135, 0.155, 0.165, 0.18, 0.18, 0.195, 0.195, 0.22, 0.22, 0.235, 0.225, 0.265, 0.28, 0.275, 0.29, 0.32, 0.3, 0.325, 0.3, 0.31, 0.325, 0.335, 0.345, 0.33, 0.34, 0.34, 0.33, 0.35, 0.36, 0.38, 0.35, 0.35, 0.38, 0.465, 0.375, 0.38, 0.39, 0.39, 0.405, 0.4, 0.4, 0.38, 0.385, 0.4, 0.38, 0.46, 0.4, 0.395, 0.4, 0.42, 0.39, 0.41, 0.415, 0.415, 0.43, 0.425, 0.43, 0.445, 0.44, 0.415, 0.455, 0.45, 0.445, 0.45, 0.47, 0.455, 0.445, 0.445, 0.455, 0.455, 0.445, 0.45, 0.47, 0.475, 0.455, 0.465, 0.475, 0.48, 0.475, 0.485, 0.485, 0.47, 0.46, 0.465, 0.475, 0.47, 0.45, 0.48, 0.485, 0.48, 0.47, 0.5, 0.495, 0.515, 0.53, 0.48, 0.5, 0.5, 0.515, 0.51, 0.525, 0.5, 0.505, 0.54, 0.52, 0.54, 0.53, 0.53, 0.56, 0.575, 0.55, 0.56, 0.55, 0.535, 0.55, 0.565, 0.505, 0.585, 0.565, 0.57, 0.625, 0.58, 0.63, 0.375, 0.205, 0.185, 0.19, 0.195, 0.215, 0.225, 0.23, 0.23, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0.225, 0.25, 0.205, 0.245, 0.25, 0.255, 0.255, 0.27, 0.255, 0.27, 0.27, 0.27, 0.265, 0.29, 0.29, 0.29, 0.275, 0.275, 0.28, 0.27, 0.275, 0.27, 0.29, 0.28, 0.28, 0.3, 0.305, 0.27, 0.29, 0.285, 0.335, 0.305, 0.325, 0.32, 0.34, 0.315, 0.34, 0.33, 0.345, 0.365, 0.33, 0.36, 0.35, 0.365, 0.34, 0.365, 0.355, 0.355, 0.355, 0.38, 0.36, 0.37, 0.355, 0.38, 0.395, 0.42, 0.375, 0.375, 0.395, 0.38, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0.395, 0.38, 0.4, 0.4, 0.395, 0.42, 0.415, 0.415, 0.41, 0.42, 0.42, 0.445, 0.405, 0.43, 0.405, 0.43, 0.435, 0.425, 0.44, 0.43, 0.425, 0.425, 0.425, 0.445, 0.455, 0.44, 0.45, 0.445, 0.39, 0.45, 0.44, 0.435, 0.42, 0.48, 0.46, 0.46, 0.455, 0.445, 0.465, 0.49, 0.475, 0.46, 0.47, 0.475, 0.45, 0.455, 0.465, 0.465, 0.475, 0.48, 0.475, 0.465, 0.455, 0.46, 0.485, 0.49, 0.435, 0.47, 0.45, 0.475, 0.465, 0.48, 0.46, 0.475, 0.5, 0.475, 0.475, 0.51, 0.495, 0.5, 0.475, 0.455, 0.505, 0.515, 0.48, 0.485, 0.505, 0.485, 0.49, 0.5, 0.495, 0.475, 0.51, 0.565, 0.525, 0.51, 0.5, 0.525, 0.505, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0.525, 0.51, 0.525, 0.535, 0.52, 0.49, 0.495, 0.515, 0.53, 0.505, 0.545, 0.545, 0.565, 0.565, 0.555, 0.57, 0.55, 0.575, 0.575, 0.6, 0.58, 0.565, 0.57, 0.61, 0.65, 0.265, 0.27, 0.28, 0.295, 0.29, 0.3, 0.29, 0.3, 0.32, 0.34, 0.34, 0.34, 0.32, 0.365, 0.355, 0.345, 0.35, 0.365, 0.365, 0.36, 0.38, 0.39, 0.38, 0.385, 0.38, 0.395, 0.425, 0.4, 0.4, 0.43, 0.4, 0.41, 0.43, 0.425, 0.43, 0.435, 0.445, 0.465, 0.46, 0.435, 0.455, 0.44, 0.465, 0.46, 0.455, 0.455, 0.485, 0.485, 0.485, 0.475, 0.48, 0.47, 0.47, 0.5, 0.455, 0.48, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0.505, 0.51, 0.515, 0.515, 0.51, 0.545, 0.5, 0.485, 0.54, 0.51, 0.505, 0.515, 0.51, 0.5, 0.525, 0.55, 0.56, 0.565, 0.575, 0.57, 0.55, 0.21, 0.215, 0.225, 0.26, 0.27, 0.265, 0.27, 0.28, 0.285, 0.29, 0.29, 0.3, 0.3, 0.325, 0.315, 0.335, 0.335, 0.35, 0.355, 0.37, 0.36, 0.36, 0.355, 0.375, 0.375, 0.4, 0.37, 0.4, 0.39, 0.375, 0.395, 0.375, 0.35, 0.395, 0.39, 0.41, 0.415, 0.38, 0.4, 0.43, 0.4, 0.425, 0.43, 0.455, 0.42, 0.425, 0.425, 0.4, 0.375, 0.42, 0.425, 0.415, 0.425, 0.435, 0.46, 0.445, 0.44, 0.425, 0.42, 0.44, 0.43, 0.45, 0.45, 0.43, 0.445, 0.425, 0.47, 0.45, 0.47, 0.445, 0.435, 0.445, 0.44, 0.435, 0.46, 0.445, 0.445, 0.49, 0.45, 0.46, 0.435, 0.45, 0.46, 0.475, 0.48, 0.48, 0.48, 0.47, 0.47, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0.455, 0.47, 0.48, 0.47, 0.505, 0.475, 0.465, 0.485, 0.47, 0.475, 0.47, 0.475, 0.485, 0.54, 0.49, 0.48, 0.45, 0.48, 0.48, 0.49, 0.5, 0.47, 0.485, 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1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0.425, 0.42, 0.41, 0.395, 0.405, 0.41, 0.41, 0.415, 0.45, 0.4, 0.43, 0.42, 0.465, 0.46, 0.47, 0.44, 0.46, 0.45, 0.49, 0.49, 0.45, 0.495, 0.47, 0.5, 0.47, 0.48, 0.49, 0.51, 0.495, 0.52, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L))), recalc_margin = 1000, save.beta = TRUE, interact = TRUE, outgraphs = FALSE, relations = c("to25", "expi", "logi", "to35", "troot", "sigmoid"), relations.prob = c(0.1, 0.525, 0.51, 0.51, 0.525, 0.53, 0.54, 0.51, 0.54, 0.54, 0.535, 0.51, 0.53, 0.55, 0.535, 0.55, 0.53, 0.6, 0.13, 0.25, 0.28, 0.355, 0.365, 0.375, 0.395, 0.4, 0.375, 0.38, 0.37, 0.415, 0.42, 0.455, 0.505, 0.475, 0.47, 0.505, 0.5, 0.525, 0.525, 0.205, 0.29, 0.33, 0.375, 0.335, 0.36, 0.37, 0.395, 0.41, 0.425, 0.425, 0.42, 0.395, 0.41, 0.4, 0.44, 0.46, 0.465, 0.43, 0.465, 0.46, 0.45, 0.42, 0.45, 0.48, 0.48, 0.455, 0.48, 0.5, 0.49, 0.475, 0.505, 0.495, 0.52, 0.525, 0.52, 0.54, 0.54, 0.58, 0.56, 0.155, 0.23, 0.23, 0.27, 0.305, 0.31, 0.365, 0.385, 0.375, 0.445, 0.34, 0.355, 0.385, 0.1, 0.1, 0.1, 0.1, 0.1), interact.param = list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, 0.445, 0.46, 0.48, 0.46, 0.47, 0.52, 0.54, 0.555, 0.42, 0.365, 0.26, 0.35, 0.325, 0.105, 0.25, 0.425, 0.41, 0.42, 0.475, 0.45, 0.45, 0.275, 0.37, 0.535, 0.295, 0.44, 0.465, 0.4, 0.255, 0.515, 0.44, 0.465, 0.475, 0.37, 0.5, 0.235, 0.395, 0.43, 0.39, 0.385, 0.44, 0.4, 0.25, 0.45, 0.5, 0.355, 0.49, 0.455, 0.425, 0.33, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list(0.5, 0.4, 0.46, 0.4, 0.355, 0.38, 0.5, 0.575, 0.5, 0.46, 0.105, 0.345, 0.43, 0.12, 0.47, 0.27, 0.18, 0.39, 0.365, 0.375, 0.405, 0.39, 0.41, 0.49, 0.43, 0.23, 0.28, 0.4, 0.28, 0.35, 0.55, 0.515, 0.44, 0.41, 0.4, 0.275, 0.42, 0.39, 0.44, 0.175, 0.365, 0.4, 0.415, 0.425, 0.355, 0.385, 0.385, 0.44, 0.55, 0.55, 0.55, 0.575, 0.205, 0.19, 0.22, 0.23, 0.285, 0.295, 0.315, 0.33, 0.33, 0.35, 0.39, 0.375, 0.41, 0.45, 0.48, 0.525, 0.545, 0.52, 0.51, 0.135, 0.175, 0.23, 0.27, 0.28, 0.31, 0.35, 0.395, 0.43, 0.465, 0.46, 0.455, 0.475, 0.505, 0.48, 0.48, 0.485, 0.515, 0.55, 0.58, 0.2, 0.28, 0.27, 0.31, 0.325, 0.325, 0.365, 0.385, 0.405, 0.41, 0.42, 0.415, 0.45, 0.46, 0.46, 0.425, 0.45, 0.46, 0.495, 0.495, 0.495, 0.47, 0.51, 0.535, 0.39, 0.405, 0.465, 0.515, 0.49, 0.275, 0.31, 0.3, 0.305, 0.335, 0.335, 0.36, 0.395, 0.4, 0.45, 0.435, 0.43, 0.385, 0.43, 0.465, 0.445, 0.48, 0.51, 0.475, 0.47, 0.365, 0.475, 0.5, 0.485, 0.485, 0.5, 0.495, 0.525, 0.52, 0.51, 0.545, 0.545, 0.545, 0.565, 0.59, 0.35, 0.365, 0.41, 0.4, 0.45, 0.48, 0.505, max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE))
30: do.call(runemjmcmc, vect[1:(length(vect) - 5)])0.525, 0.215, 0.265, 0.35, 0.365, 0.385, 0.415, 0.42, 0.445, 0.425, 0.47, 0.5, 0.495, 0.49, 0.495, 0.535, 0.505, 0.525, 0.5, 0.525, 0.53, 0.605, 0.215, 0.23, 0.25, 0.315, 0.33, 0.35, 0.37, 0.36, 0.355, 0.44, 0.475, 0.455, 0.31, 0.29, 0.3, 0.365, 0.4, 0.43, 0.43, 0.45, 0.475, 0.485, 0.555), Height = c(0.095, 0.09, 0.135, 0.125, 0.08, 0.095, 0.15, 0.125, 0.125, 0.14, 0.11, 0.135, 0.1, 0.13, 0.085, 0.1, 0.08, 0.1, 0.095, 0.1, 0.155, 0.135, 0.165, 0.14, 0.185, 0.18, 0.14, 0.165, 0.165, 0.175, 0.105, 0.155, 0.105, 0.135, 0.09, 0.105, 0.045, 0.055, 0.05, 0.095, 0.12, 0.12, 0.07,
31: doTryCatch(return(expr), name, parentenv, handler)0.12, 0.095, 0.13, 0.12, 0.1, 0.14, 0.12, 0.135, 0.06, 0.125, 0.105, 0.11, 0.13, 0.12, 0.12, 0.12, 0.07, 0.13, 0.11, 0.17, 0.175, 0.195, 0.15, 0.14, 0.17, 0.14, 0.175, 0.165, 0.14, 0.18, 0.14, 0.16, 0.13, 0.16, 0.135, 0.15, 0.175, 0.165, 0.19, 0.195, 0.13, 0.13, 0.125, 0.095, 0.145, 0.155, 0.165, 0.125, 0.125, 0.105, 0.08, 0.105, 0.115, 0.135, 0.105, 0.15, 0.085, 0.105, 0.14, 0.075, 0.08, 0.06, 0.09, 0.085, 0.16, 0.165, 0.165, 0.125, 0.09, 0.095, 0.08, 0.11, 0.095, 0.155, 0.145, 0.13, 0.125, 0.12,
32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: tryCatchList(expr, classes, parentenv, handlers)
34: tryCatch({ set.seed(as.integer(vect$cpu)) do.call(runemjmcmc, vect[1:(length(vect) - 5)]) vals = hash::values(hashStat) fparam = mySearch$fparam cterm = max(vals[1, ], na.rm = TRUE) ppp = mySearch$post_proceed_results_hash(hashStat = hashStat) post.populi = sum(exp(hash::values(hashStat)[1, ][1:vect$NM] - cterm), na.rm = TRUE) betas = NULL mliks = NULL if (vect$save.beta) { Nvars = mySearch$Nvars linx = mySearch$Nvars + 4 lHash = length(hashStat) mliks = hash::values(hashStat)[which((1:(lHash * linx))%%linx == 1)] betas = hash::values(hashStat)[which((1:(lHash * linx))%%linx == 4)] for (i in 1:(Nvars - 1)) { betas = cbind(betas, hash::values(hashStat)[which((1:(lHash * linx))%%linx == (4 + i))]) } betas = cbind(betas, hash::values(hashStat)[which((1:(lHash * linx))%%linx == (0))]) } preds = NULL if (vect$predict) { preds = mySearch$forecast.matrix.na.fast(link.g = vect$link, covariates = (vect$test), betas = betas, mliks.in = mliks)$forecast }0.08, 0.055, 0.095, 0.145, 0.185, 0.19, 0.16, 0.135, 0.13, 0.215, 0.17, 0.165, 0.165, 0.16, 0.23, 0.19, 0.175, 0.22, 0.175, 0.215, 0.145, 0.115, 0.085, 0.045, 0.15, 0.18, 0.175, 0.16, 0.19, 0.185, ret = list(post.populi = post.populi, p.post = ppp$p.post, cterm = cterm, preds = preds, fparam = fparam, betas = betas, mliks = mliks)0.16, 0.18, 0.15, 0.085, 0.14, 0.155, 0.185, 0.12, 0.165, 0.17, 0.115, 0.14, 0.11, 0.17, 0.145, 0.09, 0.14, 0.13, 0.12, 0.135, 0.13, 0.125, 0.145, 0.15, 0.15, 0.135, 0.125, 0.085, 0.165, 0.16, 0.175, 0.215, 0.075, 0.135, 0.08, 0.01, 0.03, 0.035, 0.07, 0.065, 0.08, 0.085, 0.085, 0.08, 0.085, 0.075, 0.105, 0.18, 0.155, 0.185, 0.175, 0.185, 0.16, 0.125, 0.165, 0.06, 0.08, 0.13, 0.15, 0.11, 0.15, 0.2, 0.155, 0.215, 0.185, 0.24, 0.205, 0.135, 0.185, 0.105, 0.12, 0.14, 0.175, 0.17, 0.125, 0.16, 0.165, 0.195, 0.085, 0.085, 0.14, 0.095, 0.175, 0.1, 0.1, 0.13, 0.06, 0.145, 0.19, 0.14, 0.195, 0.2, 0.175, 0.11, 0.1, 0.155, 0.095, 0.06, 0.04, 0.105, 0.09, 0.1, 0.13, 0.09, 0.12, 0.08, 0.04, 0.195, 0.19, 0.19, 0.185, 0.185, 0.185, 0.18, 0.155, 0.14, 0.08, 0.15, 0.185, 0.17, 0.14, 0.17, 0.205, 0.185, 0.18, 0.175, 0.15, 0.16, 0.145, 0.12, 0.18, 0.215, 0.195, 0.2, 0.17, 0.15, 0.195, 0.19, 0.16, 0.175, 0.185, 0.145, 0.135, 0.12, 0.135, 0.105, 0.155, 0.135, 0.125, 0.12, 0.125, 0.095, 0.095, 0.1, 0.14, 0.145, 0.15, 0.12, 0.14, 0.12, 0.115, 0.12, 0.145, 0.165, 0.12, 0.12, 0.12, 0.17, 0.14, 0.17, 0.165, 0.14, 0.165, 0.12, 0.135, 0.07, 0.175, 0.165, 0.16, 0.125, 0.175, 0.155, 0.17, 0.14, 0.1, 0.15, 0.095, 0.165, 0.11, 0.14, 0.1, 0.175, 0.15, 0.215, 0.195, 0.155, 0.14, 0.175, 0.105, 0.11, 0.135, 0.1, 0.07, 0.055, 0.07, 0.19, 0.195, 0.21, 0.2, 0.135, 0.12, 0.12, 0.16, 0.175, 0.115, 0.175, 0.225, 0.185, 0.185, 0.155, 0.125, 0.15, 0.145, 0.14, 0.15, 0.135, 0.165, 0.145, 0.13, 0.175, 0.195, 0.15, 0.175, 0.155, 0.18, 0.19, 0.19, 0.175, 0.235, 0.225, 0.18, 0.155, 0.185, 0.14, 0.145, 0.085, 0.08, 0.115, 0.08, 0.09, 0.05, 0.115, 0.055, 0.06, 0.04, 0.155, 0.185, 0.13, 0.12, 0.14, 0.13, 0.14, 0.075, 0.115, 0.14, 0.115, 0.115, 0.115, 0.105, 0.075, 0.065, 0.18, 0.16, 0.155, 0.165, 0.12, 0.125, 0.15, 0.15, 0.165, 0.145, 0.105, 0.115, 0.15, 0.13, 0.065, 0.125, 0.115, 0.14, 0.135, 0.155, 0.14, 0.15, 0.14, 0.14, 0.175, 0.175, 0.15, 0.19, 0.1, 0.17, 0.145, 0.14, 0.12, 0.15, 0.115, 0.175, 0.15, 0.18, 0.14, 0.195, 0.145, 0.11, 0.1, 0.115, 0.125, 0.09, 0.1, 0.09, 0.075, 0.125, 0.145, 0.135, 0.14, 0.085, 0.075, 0.055, 0.08, 0.14, 0.115, 0.105, 0.105, 0.16, 0.095, 0.095, 0.145, 0.115, 0.12, 0.125, 0.1, 0.09, 0.1, 0.085, 0.12, 0.13, 0.18, 0.16, 0.095, 0.13, 0.145, 0.12, 0.125, 0.065, 0.09, 0.08, 0.11, 0.165, 0.17, 0.195, 0.185, 0.18, 0.17, 0.12, 0.095, 0.12, 0.15, 0.155, 0.145, 0.145, 0.165, 0.15, 0.15, 0.115, 0.165, 0.1, 0.14, 0.115, 0.185, 0.14, 0.185, 0.125, 0.17, 0.15, 0.075, 0.12, 0.02, 0.09, 0.1, 0.12, 0.105, 0.105, 0.135, 0.125, 0.14, 0.09, 0.13, 0.08, 0.1, 0.085, 0.08, 0.065, 0.07, 0.06, 0.035, 0.025, 0.025, 0.16, 0.15, 0.13, 0.08, 0.085, 0.105, 0.125, 0.12, 0.13, 0.12, 0.125, 0.115, 0.125, 0.135, 0.09, 0.205, 0.14, 0.13, 0.13, 0.2, 0.14, 0.12, 0.17, 0.175, 0.155, 0.155, 0.12, 0.155, 0.135, 0.19, 0.145, 0.155, 0.17, 0.165, 0.14, 0.135, 0.14, 0.165, 0.145, 0.14, 0.14, 0.145, 0.15, 0.145, if (length(cterm) == 0) { vect$cpu = as.integer(vect$cpu) + as.integer(stats::runif(1, 1, 10000)) if (vect$cpu < 50000) ret = runpar.infer(vect)0.06, 0.175, 0.13, 0.135, 0.16, 0.165, 0.16, 0.135, 0.165, 0.16, 0.145, 0.155, 0.11, 0.16, 0.11, 0.135, 0.125, 0.13, 0.1, 0.135, 0.125, 0.115, 0.14, 0.055, 0.085, 0.08, 0.08, 0.09, 0.07, 0.135, 0.14, 0.095, 0.095, 0.095, 0.1, 0.11, 0.1, 0.1, 0.115, 0.1, 0.1, 0.12, 0.135, 0.105, 0.125, 0.125, 0.125, 0.13, 0.125, 0.14, 0.135, 0.15, 0.15, 0.13, 0.14, 0.165, 0.16, 0.14, 0.175, 0.155, 0.175, 0.16, 0.175, 0.15, 0.15, 0.165, 0.16, 0.165, 0.155, 0.2, 0.165, 0.17, 0.165, 0.165, 0.175, 0.165, 0.205, 0.17, 0.16, 0.17, 0.155, 0.195, 0.16, 0.22, 0.23, 0.055, 0.065, 0.05, 0.055, 0.075, 0.08, 0.08, 0.07, 0.07, 0.09, 0.075, 0.075, 0.075, 0.07, 0.1, 0.095, 0.09, 0.09, 0.115, 0.1, 0.1, 0.1, 0.09, 0.12, 0.115, 0.13, 0.11, 0.13, 0.13, 0.105, 0.11, 0.105, 0.12, 0.1, 0.115, 0.125, 0.12, 0.125, 0.115, 0.145, 0.1, 0.12, 0.115, 0.125, 0.135, 0.135, 0.135, 0.12, 0.135, 0.125, 0.13, 0.15, 0.125, 0.125, 0.135, 0.1, 0.15, 0.145, 0.155, 0.14, 0.13, 0.15, 0.135, 0.14, 0.155, 0.16, 0.13, 0.155, 0.15, 0.14, 0.17, 0.16, 0.15, 0.145, 0.16, 0.145, 0.155, 0.15, 0.165, 0.14, 0.15, 0.15, 0.155, 0.16, 0.18, 0.17, 0.175, 0.175, 0.165, 0.2, 0.155, 0.165, 0.15, 0.175, 0.185, 0.17, 0.155, 0.175, 0.175, 0.175, 0.16, 0.15, 0.16, 0.21, 0.185, 0.185, 0.19, 0.195, 0.18, 0.165, 0.225, 0.195, 0.185, 0.175, 0.175, 0.18, 0.205, 0.175, 0.185, 0.22, 0.22, 0.04, 0.04, 0.055, 0.065, 0.065, 0.055, 0.065, 0.065, 0.08, 0.055, 0.085, 0.08, 0.1, 0.1, 0.08, 0.095, 0.11, 0.1, 0.105, 0.095, 0.115, 0.12, 0.115, 0.11, 0.145, 0.12, 0.1, 0.115, 0.105, 0.125, 0.11, 0.1, 0.125, 0.125, 0.12, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.115, 0.125, 0.145, 0.125, 0.15, 0.14, 0.12, 0.125, 0.14, 0.135, 0.12, 0.14, 0.135, 0.15, 0.15, 0.145, 0.16, 0.155, 0.145, 0.15, 0.175, 0.145, 0.16, 0.165, 0.135, 0.17, 0.145, 0.15, 0.195, 0.135, 0.14, 0.15, 0.16, else ret = NULL }}, error = function(err) { print(paste0("error in thread ", vect[length(vect)])) print(err) vect$cpu = as.integer(vect$cpu) + as.integer(stats::runif(1, 1, 10000)) if (vect$cpu < 50000) 0.17, 0.15, 0.14, 0.17, 0.165, 0.16, 0.145, 0.165, 0.17, 0.15, 0.155, 0.135, 0.2, 0.16, 0.175, 0.185, 0.155, 0.175, 0.015, 0.165, 0.195, 0.17, 0.19, 0.155, 0.16, 0.175, 0.16, 0.17, 0.205, 0.215, ret = runpar.infer(vect) else ret = err}, finally = { return(ret)})
35: FUN(X[[i]], ...)
36: lapply(X = S, FUN = FUN, ...)
37: 0.165, 0.155, 0.21, 0.17, 0.185, 0.19, 0.17, 0.18, 0.19, 0.19, 0.2, 0.145, 0.185, 0.185, 0.215, 0.21, 0.21, 0.205, 0.215, 0.12, 0.06, 0.065, 0.065, 0.09, 0.065, 0.07, 0.08, 0.07, 0.075, 0.095, doTryCatch(return(expr), name, parentenv, handler)
38: tryCatchOne(expr, names, parentenv, handlers[[1L]])
0.095, 0.09, 0.09, 0.095, 0.085, 0.075, 0.09, 0.085, 0.085, 0.085, 0.085, 0.09, 0.095, 0.085, 0.09, 0.095, 0.08, 0.085, 0.11, 0.08, 0.09, 0.085, 0.09, 0.095, 0.105, 0.1, 0.1, 0.09, 0.11, 0.09, 0.11, 0.1, 0, 0.11, 0.12, 0.105, 0.105, 0.115, 0.1, 0.105, 0.105, 39: tryCatchList(expr, classes, parentenv, handlers)0.125, 0.11, 0.1, 0.18, 0.12, 0.1, 0.12, 0.11, 0.125, 0.12, 0.13, 0.12, 0.135, 0.145, 0.125, 0.14, 0.12, 0.105, 0.12, 0.135, 0.13, 0.125, 0.155, 0.11, 0.15, 0.13, 0.145, 0.14, 0.15, 0.135, 0.145,
40: tryCatch(expr, error = function(e) { call <- conditionCall(e)0.125, 0.15, 0.14, 0.135, 0.14, 0.155, 0.145, 0.135, 0.145, 0.155, 0.145, 0.175, 0.145, 0.15, 0.125, 0.135, 0.13, 0.125, 0.135, 0.165, 0.155, 0.155, 0.135, 0.15, 0.155, 0.185, 0.16, 0.165, 0.165, 0.15, 0.18, 0.155, 0.15, 0.155, 0.17, 0.155, 0.15, 0.155, 0.145, 0.155, 0.15, 0.14, 0.13, 0.16, 0.15, 0.17, 0.15, 0.17, 0.16, 0.155, 0.17, 0.165, 0.17, 0.175, 0.18, 0.15, 0.16, 0.17, 0.175, 0.16, 0.155, 0.17, 0.18, 0.145, 0.16, 0.165, 0.175, 0.17, if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]]0.155, 0.23, 0.18, 0.16, 0.195, 0.16, 0.165, 0.175, 0.17, 0.19, 0.205, 0.19, 0.175, 0.21, 0.17, 0.18, 0.175, 0.185, 0.205, 0.19, 0.515, 0.215, 0.18, 0.17, 0.17, 0.215, 0.2, 0.19, 0.205, 0.215, w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile)0.235, 0.25, 0.135, 0.095, 0.085, 0.09, 0.11, 0.09, 0.09, 0.105, 0.095, 0.105, 0.105, 0.105, 0.12, 0.11, 0.11, 0.14, 0.125, 0.115, 0.115, 0.125, 0.12, 0.085, 0.12, 0.105, 0.135, 0.125, 0.145, 0.125, 0.14, 0.13, 0.125, 0.14, 0.14, 0.16, 0.15, 0.15, 0.15, 0.15, 0.175, 0.14, 0.15, 0.15, 0.15, 0.145, 0.16, 0.155, 0.165, 0.16, 0.17, 0.13, 0.165, 0.18, 0.145, 0.175, 0.15, 0.15, 0.145, 0.185, 0.205, 0.175, 0.155, 0.175, 0.16, 0.14, 0.215, 0.215, 0.165, 0.18, 0.175, 0.185, 0.2, 0.17, 0.165, 0.2, 0.24, 0.18, 0.18, 0.055, 0.07, 0.09, 0.09, 0.095, 0.075, 0.095, 0.08, 0.095, 0.1, 0.09, 0.09, 0.09, 0.11, 0.08, 0.1, 0.11, 0.11, 0.11, 0.12, 0.145, 0.1, 0.16, 0.13, 0.115, 0.135, 0.125, 0.105, 0.12, 0.14, .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
41: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)0.13, 0.11, 0.105, 0.125, 0.125, 0.145, 0.145, 0.135, 0.155, 0.135, 0.145, 0.14, 0.14, 0.14, 0.14, 0.13, 0.135, 0.13, 0.12, 0.125, 0.15, 0.145, 0.15, 0.14, 0.145, 0.15, 0.13, 0.135, 0.155, 0.125, 0.15, 0.135, 0.14, 0.145, 0.155, 0.135, 0.155, 0.125, 0.185, 0.17, 0.15, 0.16, 0.16, 0.15, 0.155, 0.125, 0.14, 0.165, 0.145, 0.145, 0.15, 0.145, 0.15, 0.15, 0.165, 0.17, 0.18, 0.165, 0.16, 0.145, 0.145, 0.14, 0.155, 0.18, 0.155, 0.16, 0.15, 0.16, 0.16, 0.17, 0.175, 0.18, 0.165, 0.16, 0.18, 0.135, 0.15, 0.175, 0.165, 0.17, 0.155, 0.175, 0.15, 0.18, 0.17, 0.165, 0.175, 0.165, 0.17, 0.175, 0.17, 0.165, 0.18, 0.185, 0.15, 0.175, 0.19, 0.19,
42: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
43: FUN(X[[i]], ...)
44: lapply(seq_len(cores), inner.do)
0.16, 0.175, 0.15, 0.155, 0.185, 0.16, 0.18, 0.15, 0.195, 0.165, 0.175, 0.195, 0.165, 0.175, 0.16, 0.16, 0.175, 0.175, 0.16, 0.165, 0.19, 0.195, 0.16, 0.195, 0.2, 0.15, 0.175, 0.2, 0.175, 0.175, 0.215, 0.195, 0.22, 0.205, 0.18, 0.21, 0.19, 0.205, 0.215, 0.195, 0.25, 0.09, 0.135, 0.12, 0.12, 0.105, 0.135, 0.11, 0.14, 0.115, 0.155, 0.16, 0.16, 0.15, 0.155, 0.135, 0.125, 0.18, 0.2, 0.16, 0.185, 0.13, 0.145, 0.15, 0.165, 0.175, 0.15, 0.145, 0.185, 0.195, 0.15, 0.14, 0.16, 0.19, 0.19, 0.185, 0.175, 0.185, 0.195, 0.18, 0.21, 0.055, 0.075, 0.085, 0.085, 0.075, 0.105, 0.09, 0.095, 0.105, 0.09, 0.095, 0.1, 0.1, 0.11, 0.12, 0.11, 0.12, 0.105, 0.13, 0.12, 0.115, 0.135, 0.14, 0.11, 0.13, 0.145, 0.135, 0.15, 0.11, 0.14, 0.125, 0.115, 0.115, 0.12, 0.145, 0.145, 0.14, 0.16, 0.13, 0.12, 0.14, 0.14, 0.14, 0.14, 0.125, 0.13, 0.175, 0.135, 0.165, 0.125, 0.145, 0.155, 0.13, 0.12, 0.13, 0.155, 0.135, 0.155, 0.145, 0.135, 0.135, 0.155, 0.145, 0.195, 0.165, 0.15, 0.145, 0.15, 0.135, 0.17, 0.15, 0.16, 0.165, 0.145, 0.18, 0.14, 0.155, 0.145, 0.15, 0.16, 0.2, 0.15, 0.17, 0.195, 0.155, 0.155, 0.185, 0.16, 0.18, 0.145, 0.155, 0.165, 0.165, 0.155, 0.16, 0.165, 0.165, 0.165, 0.165, 0.135, 0.155, 0.18, 0.175, 0.21, 0.15, 0.155, 0.185, 0.175, 0.17, 0.205, 0.175, 0.15, 0.185, 0.19, 0.155, 0.18, 0.185, 0.175, 0.195, 0.22, 0.18, 0.195, 0.215, 0.04, 0.025, 0.055, 0.075, 0.065, 0.06, 0.07, 0.06, 0.065, 0.07, 0.07, 0.08, 0.08, 0.07, 0.105, 0.085, 0.08, 0.085, 0.095, 0.09, 0.11, 0.12, 0.105, 0.135, 0.105, 0.12, 0.12, 0.135, 0.135, 0.15, 0.13, 0.14, 0.15, 0.145, 0.15, 0.16, 0.165, 0.14, 0.14, 0.16, 0.18, 0.195, 0.06, 0.065, 0.08, 0.07, 0.085, 0.07, 0.12, 0.115, 0.115, 0.12, 0.13, 1.13, 45: parallel::mclapply(X = X, FUN = FUN, mc.preschedule = TRUE, mc.cores = mc.cores)
46: mcgmj(X = params, FUN = runpar.infer, mc.cores = n.cores)
47: pinferunemjmcmc(n.cores = M, report.level = 0.2, num.mod.best = NM, simplify = TRUE, predict = TRUE, test.data = as.data.frame(test), link.function = g, runemjmcmc.params = list(formula = formula1, data = data.example, estimator = estimate.gamma.cpen, estimator.args = list(data = data.example), recalc_margin = 1000, save.beta = TRUE, interact = TRUE, outgraphs = FALSE, relations = c("to25", "expi", "logi", "to35", "troot", "sigmoid"), relations.prob = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1), interact.param = list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, 0.12, 0.11, 0.11, 0.09, 0.14, 0.135, 0.135, 0.14, 0.13, 0.125, 0.13, 0.14, 0.13, 0.16, 0.13, 0.115, 0.135, 0.12, 0.15, 0.15, 0.14, 0.165, 0.16, 0.15, 0.14, 0.21, 0.185, 0.22, 0.215, 0.18, 0.22, 0.13, 0.115, 0.09, 0.1, 0.145, 0.1, 0.115, 0.1, 0.155, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, 0.11, 0.1, 0.125, 0.185, 0.155, 0.18, 0.225, 0.17, 0.055, 0.08, 0.13, 0.09, 0.035, 0.045, 0.05, 0.08, 0.125, 0.1, 0.12, 0.075, 0.095, 0.115, 0.1, 0.14, 0.11, 0.115, 0.1, 0.125, 0.18, 0.075, 0.105, 0.105, 0.115, 0.12, 0.09, 0.07, 0.11, 0.19, 0.075, 0.135, 0.165, 0.165, 0.175, 0.2, 0.2, 0.15, 0.16, 0.085, 0.085, 0.015, 0.075, 0.03, 0.03, 0.125, 0.185, 0.17, 0.235, 0.2, 0.25, 0.165, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list(max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE)))
48: withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))
49: suppressMessages(pinferunemjmcmc(n.cores = M, report.level = 0.2, num.mod.best = NM, simplify = TRUE, predict = TRUE, test.data = as.data.frame(test), link.function = g, runemjmcmc.params = list(formula = formula1, data = data.example, estimator = estimate.gamma.cpen, estimator.args = list(data = data.example), recalc_margin = 1000, save.beta = TRUE, interact = TRUE, outgraphs = FALSE, relations = c("to25", "expi", "logi", "to35", "troot", "sigmoid"), relations.prob = c(0.1, 0.1, 0.1, 0.1, 0.165, 0.16, 0.155, 0.125, 0.135, 0.14, 0.185, 0.165, 0.055, 0.11, 0.08, 0.09, 0.08, 0.195, 0.16, 0.19, 0.105, 0.075, 0.215, 0.18, 0.2, 0.195, 0.23, 0.12, 0.08, 0.13, 0.15, 0.145, 0.145, 0.175, 0.195, 0.18, 0.145, 0.075, 0.165, 0.18, 0.17, 0.165, 0.175, 0.185, 0.19, 0.11, 0.115, 0.12, 0.135, 0.1, 0.11, 0.12, 0.09, 0.1, 0.1), interact.param = list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, 0.165, 0.175, 0.17, 0.2, 0.18, 0.145, 0.135, 0.165, 0.16, 0.175, 0.16, 0.16, 0.175, 0.175, 0.16, 0.18, 0.21, 0.185, 0.23, 0.18, 0.13, 0.135, 0.14, 0.13, 0.12, 0.105, 0.15, 0.105, 0.09, 0.09, 0.09, 0.12, 0.145, 0.135, 0.12, 0.1, 0.125, 0.13, 0.13, 0.12, 0.135, 0.135, 0.135, 0.13, 0.13, 0.11, 0.11, 0.07, 0.145, 0.135, 0.15, 0.155, 0.135, 0.145, 0.1, 0.135, 0.105, 0.19, 0.105, 0.115, 0.135, 0.145, 0.115, 0.155, 0.195, 0.15, 0.175, 0.195, 0.2, 0.17, 0.09, 0.17, 0.195, 0.125, 0.13, 0.165, 0.105, 0.17, 0.16, 0.19, 0.175, 0.15, 0.135, 0.125, 0.21, 0.2, 0.175, 0.155, 0.195, 0.2, 0.165, 0.23, 0.225, 0.17, 0.115, 0.05, 0.11, 0.15, 0.11, 0.09, 0.11, 0.155, 0.115, 0.09, 0.04, 0.105, 0.115, 0.14, 0.14, 0.125, 0.125, 0.09, 0.16, 0.17, 0.155, 0.19, 0.17, 0.1, 0.165, 0.08, 0.145, 0.185, 0.175, 0.15, 0.115, 0.12, 0.095, 0.145, 0.105, 0.12, 0.16, 0.13, 0.11, 0.065, 0.17, 0.075, 0.095, 0.14, 0.18, 0.18, 0.16, 0.095, 0.17, 0.14, 0.14, 0.135, 0.17, 0.08, 0.09, 0.105, 0.125, 0.105, 0.08, 0.065, 0.06, 0.17, 0.14, 0.13, 0.08, 0.14, 0.14, 0.13, 0.12, 0.14, 0.08, 0.13, 0.17, 0.175, 0.175, 0.165, 0.155, 0.155, 0.18, 0.155, 0.145, 0.165, 0.175, 0.09, 0.145, 0.185, 0.145, 0.135, 0.165, 0.145, 0.1, 0.13, 0.16, 0.13, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list(max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE))))
50: eval(code, test_env)
51: eval(code, test_env)
52: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) {0.155, 0.14, 0.125, 0.08, 0.07, 0.075, 0.105, 0.075, 0.1, 0.105, 0.1, 0.105, 0.135, 0.11, 0.12, 0.135, 0.13, 0.185, 0.19, 0.15, if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
53: doTryCatch(return(expr), name, parentenv, handler)
54: tryCatchOne(expr, names, parentenv, handlers[[1L]])0.15, 0.18, 0.16, 0.185, 0.155, 0.165, 0.175, 0.185, 0.195, 0.18, 0.2, 0.175, 0.18, 0.19, 0.165, 0.065, 0.07, 0.05, 0.07, 0.055, 0.08, 0.07, 0.075, 0.1, 0.065, 0.1, 0.11, 0.115, 0.1, 0.12, 0.12, 0.12, 0.12, 0.115, 0.11, 0.105, 0.11, 0.105, 0.1, 0.145, 0.12, 0.12, 0.135, 0.125, 0.17, 0.14, 0.15, 0.14, 0.125, 0.15, 0.155, 0.145, 0.16, 0.14, 0.155, 0.15, 0.15, 0.15, 0.15, 0.16, 0.165, 0.155, 0.175, 0.175, 0.16, 0.17, 0.165, 0.2, 0.165, 0.18, 0.195, 0.175, 0.185, 0.19, 0.205, 0.155, 0.175, 0.205, 0.205, 0.215, 0.05, 0.07, 0.06, 0.075, 0.095, 0.1, 0.115, 0.1, 0.1, 0.115,
55: tryCatchList(expr, classes, parentenv, handlers)
56: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations0.11, 0.12, 0.105, 0.13, 0.12, 0.125, 0.12, 0.135, 0.155, 0.15, 0.125, 0.145, 0.12, 0.14, 0.135, 0.13, 0.13, 0.135, 0.16, 0.15, 0.13, 0.15, 0.14, 0.15, 0.14, 0.16, 0.15, 0.165, 0.14, 0.21, 0.15, 0.17, 0.15, 0.155, 0.155, 0.15, 0.155, 0.15, 0.175, 0.15, 0.16, 0.15, 0.18, 0.125, 0.175, 0.175, 0.155, 0.165, 0.165, 0.2, 0.18, 0.19, 0.165, 0.18, 0.175, 0.2, 0.19, 0.2, 0.175, 0.22, 0.03, 0.065, 0.085, 0.095, 0.08, 0.08, 0.09, 0.095, 0.1, 0.1, if (snapshot_skipped) { skip("On CRAN") }0.1, 0.11, 0.1, 0.09, 0.1, 0.11, 0.115, 0.11, 0.115, 0.095, 0.115, 0.1, 0.105, 0.115, 0.12, 0.125, 0.125, 0.125, 0.145, 0.11, 0.13, 0.15, 0.13, 0.12, 0.155, 0.15, 0.145, 0.145, 0.155, 0.135, 0.15, else if (!new_expectations && skip_on_empty) { skip_empty() }0.145, 0.175, 0.14, 0.135, 0.15, 0.155, 0.175, 0.145, 0.15, 0.165, 0.145, 0.13, 0.14, 0.14, 0.09, 0.185, 0.145, 0.185, 0.13, 0.15, 0.14, 0.165, 0.14, 0.165, 0.175, 0.155, 0.15, 0.195, 0.155, 0.17, 0.155, 0.175, 0.19, 0.2, 0.17, 0.205, 0.2, 0.205, 0.19, 0.05, 0.055, 0.09, 0.075, 0.075, 0.09, 0.09, 0.105, 0.115, 0.12, 0.105, 0.145, 0.135, 0.14, 0.14, 0.13, 0.14, 0.16, 0.135, 0.155, 0.15, 0.155, 0.16, 0.175, 0.16, 0.19, 0.17, 0.175, 0.165, 0.16, 0.16, 0.155, 0.17, 0.19, 0.195, 0.18, 0.18, 0.21, 0.19, 0.07, 0.075, 0.08, 0.075, 0.085, 0.09, 0.095, 0.1, 0.095, 0.09, 0.14, 0.11, 0.13, 0.12, 0.12, 0.125, 0.125, 0.115, 0.145, 0.15, 0.15, 0.125, 0.13, 0.13, 0.15, 0.165, 0.13, 0.14, 0.13, 0.13, 0.15, 0.16, 0.15, 0.15, 0.15, 0.15, 0.125, 0.125, 0.145, 0.155, 0.135, 0.17, 0.195, 0.17, 0.155, 0.155, 0.195, 0.14, 0.16, 0.175, 0.165, 0.15, 0.195, 0.175, 0.165, 0.16, 0.12, 0.18, 0.185, 0.17, 0.19, 0.175, 0.18, 0.18, 0.17, 0.19, 0.195, 0.2, 0.205, 0.2, 0.18, 0.175, 0.19, 0.175, 0.16, 0.175, 0.205, 0.2, 0.095, 0.115, 0.1, 0.155, }, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")0.155, 0.115, 0.13, 0.21, 0.135, 0.19, 0.15, 0.135, 0.15, 0.165, 0.17, 0.17, 0.17, 0.18, 0.16, 0.165, 0.175, 0.17, 0.21, 0.225, 0.08, 0.09, 0.09, 0.11, 0.105, 0.105, 0.105, 0.115, 0.125, 0.105, 0.11, 0.125, 0.11, 0.1, 0.115, 0.135, 0.125, 0.14, 0.115, 0.11, 0.145, 0.12, 0.19, 0.15, 0.145, 0.135, 0.14, 0.13, 0.14, 0.16, 0.175, 0.15, 0.175, 0.14, 0.16, 0.15, 0.16, 0.215, 0.16, 0.155, 0.175, 0.18, 0.165, 0.18, 0.145, 0.15, 0.17, 0.175, 0.165, 0.16, 0.17, 0.18, 0.155, 0.145, 0.185, 0.195, 0.165, 0.2, 0.205, 0.2, 0.05, 0.05, 0.06, 0.075, 0.095, 0.085, 0.12, 0.125, 0.15, 0.12, 0.115, 0.15, 0.125, 0.145, 0.18, 0.2, 0.06, 0.065, 0.1, 0.085, 0.12, 0.105, 0.115, 0.105, 0.1, 0.135, 0.145, 0.135, 0.15, 0.155, 0.155, 0.19, 0.165, 0.16, 0.17, 0.1, 0.125, 0.125, 0.125, 0.105, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
57: 0.14, 0.09, 0.16, 0.08, 0.055, 0.2, 0.17, 0.15, 0.18, 0.165, 0.175, 0.105, 0.13, 0.125, 0.09, 0.11, 0.065, 0.07, 0.14, 0.125, 0.19, 0.115, 0.155, 0.155, 0.155, 0.16, 0.12, 0.135, 0.1, 0.07, 0.135, 0.115, 0.11, 0.135, 0.23, 0.025, 0.18, 0.115, 0.185, 0.075, 0.075, 0.14, 0.155, 0.16, 0.1, 0.22, 0.185, 0.185, 0.075, 0.125, 0.14, 0.165, 0.17, 0.14, 0.16, 0.175, 0.12, 0.19, 0.145, 0.195, 0.175, 0.12, 0.13, 0.135, 0.125, 0.19, 0.15, 0.19, 0.155, 0.1, 0.155, 0.15, 0.185, 0.135, 0.19, 0.19, 0.16, 0.205, 0.16, 0.16, doWithOneRestart(return(expr), restart)
58: withOneRestart(expr, restarts[[1L]])
59: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) {0.19, 0.165, 0.09, 0.125, 0.125, 0.13, 0.105, 0.105, 0.075, 0.15, 0.11, 0.125, 0.165, 0.125, 0.19, 0.115, 0.14, 0.135, 0.135, 0.115, 0.125, 0.145, 0.13, 0.125, 0.14, 0.14, 0.11, 0.125, 0.14, 0.19, skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) {0.185, 0.165, 0.13, 0.16, 0.12, 0.13, 0.16, 0.12, 0.165, 0.175, 0.205, 0.19, 0.175, 0.14, 0.175, 0.135, 0.08, 0.12, 0.165, 0.135, 0.15, 0.11, 0.115, 0.09, 0.065, 0.2, 0.155, 0.14, 0.105, 0.1, 0.09, 0.2, 0.17, 0.15, 0.125, 0.18, 0.125, 0.125, 0.135, 0.125, 0.18, 0.175, 0.185, 0.145, 0.135, 0.095, 0.11, 0.145, 0.145, 0.1, 0.115, 0.105, 0.13, 0.14, 0.11, 0.11, 0.075, 0.175, 0.1, 0.15, 0.085, 0.115, 0.195, 0.17, 0.125, 0.09, 0.155, 0.15, 0.09, 0.075, 0.08, 0.165, 0.12, 0.13, 0.115, 0.125, 0.145, 0.185, 0.15, 0.16, 0.125, 0.185, 0.13, 0.16, 0.16, 0.085, 0.115, 0.15, 0.15, 0.11, 0.105, 0.075, 0.075, 0.08, 0.09, 0.095, 0.105, 0.12, 0.105, 0.16, 0.15, 0.18, 0.135, 0.165, 0.15, 0.2, 0.2, 0.065, 0.065, 0.075, 0.105, 0.09, 0.095, 0.09, 0.105, 0.125, 0.1, 0.115, 0.13, 0.1, 0.11, 0.12, 0.12, 0.125, 0.125, 0.155, 0.145, 0.11, 0.165, 0.155, 0.16, 0.16, 0.19, 0.165, 0.16, 0.17, 0.155, 0.195, 0.18, 0.165, 0.23, 0.185, 0.085, 0.1, 0.11, 0.11, 0.105, 0.145, 0.115, 0.16, 0.115, 0.16, 0.13, 0.16, 0.145, 0.15, 0.135, 0.13, 0.145, 0.145, 0.145, 0.165, 0.145, 0.16, 0.175, 0.19, 0.18, 0.18, 0.175, 0.165, 0.175, 0.175, 0.18, 0.175, 0.175, 0.18, 0.19, 0.2, 0.175, 0.195, 0.18, 0.185, 0.07, 0.085, 0.085, 0.085, 0.085, 0.09, 0.09, 0.09, 0.115, 0.09, 0.1, 0.11, 0.105, 0.12, 0.095, 0.11, 0.09, 0.125, 0.115, 0.125, 0.125, 0.115, 0.135, 0.13, 0.135, 0.13, 0.135, 0.155, 0.175, 0.155, 0.13, 0.155, 0.125, 0.16, 0.155, 0.14, 0.15, 0.165, 0.165, 0.165, 0.145, 0.15, 0.23, 0.17, 0.145, snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")0.165, 0.16, 0.16, 0.175, 0.165, 0.16, 0.15, 0.185, 0.18, 0.195, 0.165, 0.16, 0.165, 0.155, 0.2, 0.145, 0.17, 0.18, 0.22, 0.04, 0.11, 0.115, 0.11, 0.155, 0.16, 0.17, 0.18, 0.17, 0.155, 0.2, 0.17, 0.17, 0.17, 0.165, 0.175, 0.195, 0.19, 0.2, 0.05, 0.07, 0.085, 0.075, 0.065, 0.085, 0.09, 0.1, 0.105, 0.095, 0.12, 0.115, 0.12, 0.12, 0.105, 0.1, 0.125, 0.125, 0.13, 0.125, 0.13, 0.14, 0.125, 0.125, 0.14, 0.14, 0.125, 0.15, 0.14, 0.135, 0.15, 0.155, 0.145, 0.14, 0.135, 0.135, 0.155, 0.165, 0.165, 0.15, 0.19, 0.165, 0.175, 0.18, 0.155, 0.165, 0.17, 0.19, 0.17, 0.195, 0.205, 0.175, 0.175, 0.19, 0.17, 0.195, 0.155, 0.195, 0.2, 0.185, 0.17, 0.195, 0.19, 0.175, 0.21, 0.17, 0.21, 0.075, 0.1, 0.095, 0.12, 0.15, 0.155, 0.145, 0.15, 0.15, 0.135, 0.115, 0.18, 0.19, 0.16, 0.165, 0.15, 0.15, 0.17, 0.17, 0.195, 0.205, 0.065, 0.1, 0.095, 0.11, }, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
60: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
61: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
62: FUN(X[[i]], ...)
63: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
64: doTryCatch(return(expr), name, parentenv, handler)
65: 0.115, 0.11, 0.14, 0.105, 0.14, 0.155, 0.13, 0.135, 0.115, 0.15, 0.145, 0.14, 0.16, 0.145, 0.125, 0.12, 0.15, 0.155, 0.155, 0.125, 0.185, 0.185, 0.13, 0.155, 0.185, 0.16, 0.15, 0.175, 0.175, 0.175, 0.18, 0.175, 0.175, 0.21, 0.2, 0.17, 0.045, 0.075, 0.06, 0.1, 0.11, 0.095, 0.115, 0.13, 0.135, 0.13, 0.1, 0.12, 0.12, 0.14, tryCatchOne(expr, names, parentenv, handlers[[1L]])
66: tryCatchList(expr, classes, parentenv, handlers)
67: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
68: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
69: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
70: 0.155, 0.175, 0.17, 0.17, 0.185, 0.195, 0.17, 0.175, 0.115, 0.075, 0.1, 0.1, 0.035, 0.095, 0.125, 0.145, 0.125, 0.145, 0.16, 0.155, 0.085, 0.125, 0.22, 0.11, 0.13, 0.155, 0.155, 0.085, 0.2, 0.19, 0.15, 0.175, 0.125, 0.18, 0.065, 0.165, 0.165, 0.13, 0.16, 0.165, 0.115, 0.075, 0.16, 0.225, 0.115, 0.17, 0.13, 0.12, 0.105, 0.2, 0.14, 0.18, 0.15, 0.125, 0.135, 0.18, 0.19, 0.165, 0.15, 0.035, test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
0.14, 0.125, 0.02, 0.165, 0.095, 0.065, 0.15, 0.135, 0.125, 0.14, 0.14, 0.16, 0.19, 0.135, 0.085, 0.095, 0.14, 0.08, 0.115, 0.22, 0.215, 0.165, 0.115, 0.115, 0.065, 0.145, 0.105, 0.16, 0.065, 0.14, 0.165, 0.15, 0.13, 0.12, 0.105, 0.12, 0.155, 0.19, 0.195, 0.205, 0.195, 0.075, 0.06, 0.07, 0.07, 0.09, 0.095, 0.12, 0.1, 0.135, 0.135, 0.12, 0.115, 0.115, 0.125, 0.185, 0.17, 0.185, 0.195, 0.2, 0.045, 0.055, 0, 0.09, 0.08, 0.095, 0.105, 0.125, 0.14, 0.15, 0.15, 0.155, 0.15, 0.175, 0.175, 0.145, 0.155, 0.155, 0.175, 0.195, 0.07, 0.09, 0.09, 0.1, 0.1, 0.11, 0.09, 0.125, 0.125, 0.14, 0.14, 0.155, 0.12, 0.165, 0.135, 0.155, 0.16, 0.155, 0.16, 0.155, 0.175, 0.145, 0.165, 0.185, 0.12, 0.13, 0.145, 0.16, 0.18, 0.08, 0.085, 0.115, 0.1, 0.105, 0.125, 0.12, 0.125, 0.14, 0.15, 0.125, 0.18, 0.13, 0.125, 0.15, 0.16, 0.17, 0.16, 0.155, 0.13, 0.155, 0.205, 0.18, 0.18, 0.155, 0.18, 0.16, 0.18, 0.175, 0.15, 0.185, 0.215, 0.175, 0.18, 0.205, 0.1, 0.13, 0.155, 0.14, 0.15, 0.16, 0.175, 0.185, 0.05, 0.09, 0.13, 0.11, 0.12, 0.135, 0.145, 0.11, 0.14, 0.17, 0.17, 0.155, 0.155, 0.175, 0.19, 0.165, 71: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
72: test_check("EMJMCMC")
An irrecoverable exception occurred. R is aborting now ...
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0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 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0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L)), Age ~ 1 + I(Length) + I(Diameter) + I(Height) + I(WholeWeight) + I(ShuckedWeight) + I(VisceraWeight) + I(ShellWeight))
10: do.call(estimator, c(estimator.args, model$formula))
11: withVisible(...elt(i))
12: utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))})
13: tryCatchList(expr, classes, parentenv, handlers)
14: tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))}))
15: doWithOneRestart(return(expr), restart)
16: withOneRestart(expr, restarts[[1L]])
17: withRestarts(tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula))})), abort = function() { onerr <- TRUE fm <- NULL})
18: withVisible(...elt(i))
19: utils::capture.output({ withRestarts(tryCatch(utils::capture.output({ fm <- do.call(estimator, c(estimator.args, model$formula)) })), abort = function() { onerr <- TRUE fm <- NULL })})
20: FUN(X[[i]], ...)
21: parallelize(X = vect, FUN = .self$fitmodel)
22: tryCatchList(expr, classes, parentenv, handlers)
23: tryCatch({ if (printable.opt) print(paste("proceed with layer", layer)) if (printable.opt) print(paste("current solution is", as.character(varcand))) vect <- buildmodel(max.cpu = layer, varcur.old = varcurb, statid = model$statid, switch.type = 5, min.N = min.N, max.N = max.N) if (printable.opt) print(paste("finish preparing models at layer", layer)) cluster <- TRUE flag1 <- 0 for (mod_id in 1:layer) { if (is.null(vect[[mod_id]]$formula)) { flag1 <- flag1 + 1 } } if (flag1 == layer) { cluster <- FALSE if (printable.opt) print("!!!!forward Models already estimated!!!!") } else { res.par <- parallelize(X = vect, FUN = .self$fitmodel) } if (printable.opt) print(paste("end forward optimizing at layer", layer)) for (mod_id in 1:layer) { if (cluster) { fmb <- fm fm <- res.par[[mod_id]] waiccand <- Inf if (is.null(fm) && (is.na(res.par[[mod_id]]$waic))) { varcand <- varcurb if (printable.opt) print("forward Model Fit Error!?") next } } varcand <- vect[[mod_id]]$varcur if (cluster) { waiccand <- res.par[[mod_id]]$waic mlikcand <- res.par[[mod_id]]$mlik } else if (exists("statistics1")) { iidd <- bittodec(varcand) + 1 waiccand <- statistics1[iidd, 2] mlikcand <- statistics1[iidd, 1] } else if (exists("hashStat")) { iidd <- paste(varcand, collapse = "") waiccand <- hash::values(hashStat[iidd])[2] mlikcand <- hash::values(hashStat[iidd])[1] } if (objective == 0) { objcand <- waiccand objcur <- waiccur objglob <- waicglob } else { objcand <- -mlikcand objcur <- -mlikcur objglob <- -mlikglob } if (objcand <= objcur || mod_id == 1) { if (printable.opt) print(paste("forward accept with ", objcand)) objcur <- objcand varcurb <- varcand waiccur <- waiccand varcur <- varcand mlikcur <- mlikcand if (objcur < objglob) { objglob <- objcur waicglob <- waiccand varglob <- varcand mlikglob <- mlikcand if (!is.null(fm)) { modglob <- fm } if (printable.opt) print(paste("forward global optima with ", objcand)) } } } if (objcur != objglob) { if (model$locstop) { break } else { varcurb <- varcur } }})
24: doWithOneRestart(return(expr), restart)
25: withOneRestart(expr, restarts[[1L]])
26: withRestarts(tryCatch({ if (printable.opt) print(paste("proceed with layer", layer)) if (printable.opt) print(paste("current solution is", as.character(varcand))) vect <- buildmodel(max.cpu = layer, varcur.old = varcurb, statid = model$statid, switch.type = 5, min.N = min.N, max.N = max.N) if (printable.opt) print(paste("finish preparing models at layer", layer)) cluster <- TRUE flag1 <- 0 for (mod_id in 1:layer) { if (is.null(vect[[mod_id]]$formula)) { flag1 <- flag1 + 1 } } if (flag1 == layer) { cluster <- FALSE if (printable.opt) print("!!!!forward Models already estimated!!!!") } else { res.par <- parallelize(X = vect, FUN = .self$fitmodel) } if (printable.opt) print(paste("end forward optimizing at layer", layer)) for (mod_id in 1:layer) { if (cluster) { fmb <- fm fm <- res.par[[mod_id]] waiccand <- Inf if (is.null(fm) && (is.na(res.par[[mod_id]]$waic))) { varcand <- varcurb if (printable.opt) print("forward Model Fit Error!?") next } } varcand <- vect[[mod_id]]$varcur if (cluster) { waiccand <- res.par[[mod_id]]$waic mlikcand <- res.par[[mod_id]]$mlik } else if (exists("statistics1")) { iidd <- bittodec(varcand) + 1 waiccand <- statistics1[iidd, 2] mlikcand <- statistics1[iidd, 1] } else if (exists("hashStat")) { iidd <- paste(varcand, collapse = "") waiccand <- hash::values(hashStat[iidd])[2] mlikcand <- hash::values(hashStat[iidd])[1] } if (objective == 0) { objcand <- waiccand objcur <- waiccur objglob <- waicglob } else { objcand <- -mlikcand objcur <- -mlikcur objglob <- -mlikglob } if (objcand <= objcur || mod_id == 1) { if (printable.opt) print(paste("forward accept with ", objcand)) objcur <- objcand varcurb <- varcand waiccur <- waiccand varcur <- varcand mlikcur <- mlikcand if (objcur < objglob) { objglob <- objcur waicglob <- waiccand varglob <- varcand mlikglob <- mlikcand if (!is.null(fm)) { modglob <- fm } if (printable.opt) print(paste("forward global optima with ", objcand)) } } } if (objcur != objglob) { if (model$locstop) { break } else { varcurb <- varcur } }}), abort = function() { varcur <- varcurb fm <- fmb closeAllConnections() withr::local_options(error = traceback) onerr <- TRUE})
27: forward_selection(list(varcur = rep(0, length(fparam.example)), mlikcur = -Inf, waiccur = Inf, locstop = glob.model$locstop, statid = -1))
28: mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models * 100, trest = n.models, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq))
29: (function (formula, data, secondary = vector(mode = "character", length = 0), latnames = "", estimator, estimator.args = "list", n.models, p.add.default = 1, p.add = 0.5, unique = FALSE, save.beta = FALSE, locstop.nd = FALSE, latent = "", max.cpu = 4, max.cpu.glob = 2, create.table = TRUE, hash.length = 20, presearch = TRUE, locstop = FALSE, pseudo.paral = FALSE, interact = FALSE, deep.method = 1, relations = c("", "sin", "cos", "sigmoid", "tanh", "atan", "erf"), relations.prob = c(0.4, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1), gen.prob = c(1, 10, 5, 1, 0), pool.cross = 0.9, p.epsilon = 1e-04, del.sigma = 0.5, pool.cor.prob = FALSE, interact.param = list(allow_offsprings = 2, mutation_rate = 100, last.mutation = 2000, max.tree.size = 10000, Nvars.max = 100, p.allow.replace = 0.7, p.allow.tree = 0.1, p.nor = 0.3, p.and = 0.7), prand = 0.01, keep.origin = TRUE, sup.large.n = 5000, recalc_margin = 2^10, create.hash = FALSE, interact.order = 1, burn.in = 1, eps = 10^6, max.time = 120, max.it = 25000, print.freq = 100, outgraphs = FALSE, advanced.param = NULL, distrib_of_neighbourhoods = t(array(data = c(7.6651604, 16.773326, 14.541629, 12.839445, 2.964227, 13.048343, 7.165434, 0.9936905, 15.94249, 11.040131, 3.200394, 15.349051, 5.466632, 14.676458, 1.5184551, 9.285762, 6.125034, 3.627547, 13.343413, 2.923767, 15.318774, 14.529538, 1.52196, 11.804457, 5.070282, 6.93438, 10.578945, 12.455602, 6.0826035, 2.453729, 14.340435, 14.863495, 1.028312, 12.685017, 13.806295), dim = c(7, 5))), distrib_of_proposals = c(76.9187, 71.25264, 87.68184, 60.55921, 15812.39852), quiet = TRUE) { global_env <- as.environment(1L) assign("data.example", data, envir = global_env) variables <- simplify.formula(formula, names(data.example)) assign("fparam.example", variables$fparam, envir = global_env) assign("fobserved.example", variables$fobserved, envir = global_env) fparam.tmp <- as.vector(sapply(FUN = paste, "I(", variables$fparam, ")", sep = "")[, 1]) if (latnames[1] != "") assign("fparam.example", c(fparam.tmp, latnames), envir = global_env) else assign("fparam.example", fparam.tmp, envir = global_env) assign("mySearch", methods::new(structure("EMJMCMC2016", package = "EMJMCMC")), envir = global_env) if (length(secondary) > 0) mySearch$filtered <- sapply(FUN = paste, "I(", secondary, ")", sep = "") mySearch$estimator <- estimator mySearch$latnames <- latnames mySearch$estimator.args <<- estimator.args mySearch$latent.formula <- latent mySearch$save.beta <- save.beta mySearch$prand <- prand mySearch$p.add <- array(p.add, length(fparam.example)) mySearch$p.add.default <- p.add.default mySearch$recalc.margin <<- as.integer(recalc_margin) mySearch$max.cpu <<- as.integer(max.cpu) mySearch$locstop.nd <- locstop.nd mySearch$pool.cor.prob <- pool.cor.prob mySearch$sup.large.n <- as.integer(sup.large.n) mySearch$max.cpu.glob <<- as.integer(max.cpu.glob) mySearch$deep.method <- as.integer(deep.method) if (interact) { mySearch$allow_offsprings <<- as.integer(interact.param$allow_offsprings) mySearch$mutation_rate <<- as.integer(interact.param$mutation_rate) mySearch$Nvars.max <<- as.integer(interact.param$Nvars.max) mySearch$max.tree.size <- as.integer(interact.param$max.tree.size) mySearch$p.allow.replace <- interact.param$p.allow.replace mySearch$p.allow.tree <<- interact.param$p.allow.tree mySearch$p.epsilon <- p.epsilon mySearch$keep.origin <- keep.origin mySearch$sigmas <<- relations mySearch$sigmas.prob <<- relations.prob mySearch$del.sigma <- del.sigma mySearch$pool.cross <- pool.cross mySearch$gen.prob <<- gen.prob mySearch$p.nor <- interact.param$p.nor mySearch$p.and <- interact.param$p.and mySearch$last.mutation <- as.integer(interact.param$last.mutation) } if (!is.null(advanced.param)) { mySearch$max.N.glob <<- as.integer(advanced.param$max.N.glob) mySearch$min.N.glob <<- as.integer(advanced.param$min.N.glob) mySearch$max.N <<- as.integer(advanced.param$max.N) mySearch$min.N <- as.integer(advanced.param$min.N) mySearch$printable.opt <- advanced.param$printable } if (exists("hashStat")) { hash::clear(hashStat) remove(hashStat, envir = global_env) } if (exists("statistics1")) { remove(statistics, envir = global_env) remove(statistics1, envir = global_env) } if (exists("hash.keys1")) { remove(hash.keys, envir = global_env) remove(hash.keys1, envir = global_env) } if (create.table) { if (pseudo.paral) mySearch$parallelize <- lapply assign("statistics1", bigmemory::big.matrix(nrow = 2^min((length(fparam.example)), hash.length) + 1, ncol = 16 + length(fparam.example) * save.beta, init = NA, type = "double"), envir = global_env) assign("statistics", bigmemory::describe(statistics1), envir = global_env) mySearch$g.results[4, 1] <- 0 mySearch$g.results[4, 2] <- 0 mySearch$p.add <- array(data = 0.5, dim = length(fparam.example)) if ((length(fparam.example)) > 20) { mySearch$hash.length <- as.integer(hash.length) mySearch$double.hashing <- TRUE assign(hash.keys1, bigmemory::big.matrix(nrow = 2^(hash.length) + 1, ncol = length(fparam.example), init = 0, type = "char"), global_env) assign(hash.keys, bigmemory::describe(hash.keys1), global_env) } } else if (create.hash) { assign("hashStat", hash::hash(), envir = global_env) mySearch$parallelize <<- lapply mySearch$hash.length <- as.integer(20) mySearch$double.hashing <<- FALSE } initsol = stats::rbinom(n = length(fparam.example), size = 1, prob = 0.5) if (unique) resm <- mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models * 100, trest = n.models, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq)) else resm <- mySearch$modejumping_mcmc(list(varcur = initsol, locstop = locstop, presearch = presearch, statid = 5, distrib_of_proposals = distrib_of_proposals, distrib_of_neighbourhoods = distrib_of_neighbourhoods, eps = eps, trit = n.models, trest = n.models * 100, burnin = burn.in, max.time = max.time, maxit = max.it, print.freq = print.freq)) ppp <- 1 if (!quiet) message("MJMCMC is completed") if (create.table) { ppp <- mySearch$post_proceed_results(statistics1 = statistics1) truth = ppp$p.post truth.m = ppp$m.post truth.prob = ppp$s.mass ordering = sort(ppp$p.post, index.return = TRUE) } else if (create.hash) { ppp <- mySearch$post_proceed_results_hash(hashStat = hashStat) truth = ppp$p.post truth.m = ppp$m.post truth.prob = ppp$s.mass ordering = sort(ppp$p.post, index.return = TRUE) } if (!quiet) { message("Post Proceed Results") cat("pi truth", sprintf("%.10f", truth[ordering$ix]), sprintf(fparam.example[ordering$ix]), sep = "\n") } if (outgraphs) { withr::local_par(mar = c(10, 4, 4, 2) + 4.1) graphics::barplot(resm$bayes.results$p.post, density = 46, border = "black", main = "Marginal Inclusion (RM)", ylab = "Probability", names.arg = mySearch$fparam, las = 2) graphics::barplot(resm$p.post, density = 46, border = "black", main = "Marginal Inclusion (MC)", ylab = "Probability", names.arg = mySearch$fparam, las = 2) } return(ppp)})(formula = Age ~ 1 + Length + Diameter + Height + WholeWeight + ShuckedWeight + VisceraWeight + ShellWeight + Male + Femele, data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 7L, 6L, 8L, 7L, 10L, 10L, 7L, 8L, 8L, 8L, 4L, 7L, 7L, 9L, 10L, 8L, 8L, 10L, 6L, 13L, 8L, 20L, 11L, 13L, 15L, 9L, 14L, 9L, 12L, 16L, 14L, 12L, 13L, 10L, 9L, 12L, 15L, 12L, 13L, 10L, 15L, 14L, 8L, 7L, 10L, 7L, 15L, 12L, 11L, 10L, 9L, 9L, 9L, 9L, 11L, 11L, 10L, 9L, 8L, 9L, 14L, 6L, 6L, 5L, 6L, 8L, 19L, 18L, 17L, 9L, 7L, 7L, 7L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 9L, 5L, 4L, 15L, 9L, 10L, 10L, 12L, 10L, 16L, 13L, 13L, 13L, 13L, 16L, 14L, 20L, 14L, 12L, 14L, 7L, 8L, 8L, 4L, 11L, 14L, 21L, 10L, 13L, 10L, 11L, 13L, 14L, 8L, 10L, 12L, 16L, 9L, 13L, 13L, 8L, 9L, 8L, 13L, 10L, 7L, 10L, 8L, 7L, 10L, 8L, 9L, 11L, 9L, 11L, 9L, 7L, 7L, 11L, 15L, 14L, 22L, 7L, 12L, 9L, 1L, 3L, 5L, 5L, 5L, 8L, 8L, 10L, 13L, 9L, 7L, 7L, 13L, 12L, 15L, 15L, 19L, 13L, 12L, 11L, 4L, 6L, 11L, 14L, 8L, 9L, 16L, 13L, 22L, 12L, 18L, 20L, 11L, 15L, 7L, 9L, 14L, 14L, 10L, 9L, 10L, 15L, 26L, 6L, 4L, 11L, 9L, 13L, 8L, 6L, 10L, 4L, 10L, 21L, 14L, 19L, 23L, 23L, 8L, 10L, 18L, 6L, 5L, 4L, 7L, 7L, 7L, 12L, 8L, 8L, 5L, 5L, 16L, 11L, 14L, 16L, 13L, 12L, 12L, 8L, 13L, 6L, 14L, 8L, 22L, 12L, 9L, 20L, 13L, 18L, 20L, 16L, 19L, 11L, 10L, 12L, 17L, 16L, 19L, 14L, 13L, 20L, 11L, 10L, 15L, 12L, 10L, 10L, 12L, 10L, 9L, 12L, 10L, 14L, 9L, 10L, 9L, 6L, 11L, 10L, 8L, 11L, 9L, 10L, 7L, 8L, 16L, 11L, 15L, 14L, 11L, 12L, 14L, 15L, 20L, 20L, 13L, 14L, 11L, 13L, 8L, 13L, 18L, 19L, 21L, 18L, 20L, 18L, 22L, 13L, 11L, 14L, 13L, 10L, 19L, 10L, 12L, 18L, 16L, 17L, 11L, 11L, 15L, 9L, 10L, 11L, 11L, 6L, 5L, 6L, 12L, 13L, 17L, 21L, 9L, 10L, 9L, 10L, 17L, 9L, 17L, 21L, 16L, 29L, 17L, 15L, 19L, 12L, 13L, 15L, 11L, 14L, 14L, 13L, 15L, 17L, 15L, 12L, 11L, 12L, 13L, 17L, 15L, 12L, 15L, 11L, 16L, 10L, 10L, 10L, 6L, 6L, 9L, 5L, 4L, 3L, 9L, 5L, 4L, 3L, 14L, 20L, 13L, 12L, 10L, 11L, 11L, 7L, 10L, 15L, 11L, 15L, 8L, 10L, 11L, 8L, 11L, 17L, 13L, 12L, 13L, 9L, 10L, 14L, 12L, 13L, 8L, 11L, 11L, 14L, 10L, 12L, 11L, 16L, 14L, 20L, 17L, 10L, 11L, 10L, 10L, 17L, 14L, 19L, 11L, 10L, 13L, 14L, 13L, 13L, 9L, 18L, 12L, 13L, 13L, 16L, 17L, 10L, 13L, 11L, 13L, 9L, 10L, 12L, 9L, 18L, 15L, 13L, 12L, 7L, 9L, 7L, 10L, 18L, 12L, 17L, 15L, 12L, 10L, 8L, 21L, 9L, 7L, 10L, 9L, 8L, 7L, 6L, 14L, 13L, 13L, 19L, 7L, 9L, 9L, 11L, 9L, 5L, 6L, 7L, 10L, 17L, 16L, 18L, 11L, 18L, 11L, 19L, 10L, 11L, 15L, 13L, 13L, 14L, 19L, 21L, 23L, 11L, 23L, 7L, 10L, 7L, 16L, 10L, 15L, 16L, 11L, 11L, 5L, 9L, 4L, 7L, 5L, 13L, 16L, 12L, 14L, 9L, 10L, 11L, 10L, 7L, 11L, 8L, 9L, 7L, 8L, 6L, 4L, 2L, 3L, 13L, 15L, 15L, 11L, 10L, 12L, 13L, 15L, 11L, 18L, 10L, 12L, 12L, 14L, 10L, 14L, 17L, 20L, 17L, 17L, 9L, 10L, 13L, 20L, 20L, 13L, 15L, 16L, 17L, 14L, 14L, 11L, 17L, 11L, 8L, 9L, 10L, 11L, 15L, 13L, 15L, 15L, 12L, 10L, 5L, 15L, 9L, 12L, 9L, 12L, 10L, 10L, 12L, 12L, 15L, 11L, 9L, 11L, 11L, 10L, 10L, 9L, 7L, 10L, 15L, 12L, 12L, 6L, 7L, 7L, 6L, 6L, 6L, 7L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 9L, 7L, 7L, 8L, 8L, 9L, 8L, 9L, 9L, 8L, 9L, 11L, 10L, 10L, 10L, 10L, 9L, 9L, 8L, 12L, 11L, 10L, 11L, 9L, 12L, 10L, 9L, 12L, 11L, 9L, 11L, 11L, 14L, 9L, 9L, 8L, 11L, 9L, 17L, 15L, 10L, 12L, 9L, 11L, 11L, 11L, 17L, 5L, 3L, 6L, 4L, 4L, 5L, 4L, 5L, 8L, 5L, 7L, 7L, 5L, 7L, 7L, 6L, 7L, 6L, 5L, 6L, 6L, 6L, 7L, 8L, 6L, 6L, 6L, 7L, 7L, 8L, 7L, 7L, 6L, 7L, 7L, 6L, 8L, 9L, 6L, 6L, 8L, 7L, 7L, 8L, 8L, 7L, 6L, 8L, 7L, 7L, 9L, 8L, 9L, 8L, 8L, 7L, 8L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 8L, 10L, 8L, 8L, 7L, 9L, 10L, 8L, 8L, 8L, 11L, 11L, 9L, 9L, 9L, 11L, 10L, 8L, 11L, 10L, 10L, 10L, 9L, 9L, 8L, 11L, 8L, 11L, 10L, 11L, 10L, 11L, 8L, 11L, 10L, 10L, 9L, 11L, 10L, 11L, 9L, 10L, 6L, 11L, 10L, 12L, 12L, 8L, 10L, 12L, 11L, 10L, 11L, 11L, 12L, 4L, 4L, 4L, 4L, 5L, 7L, 6L, 5L, 6L, 6L, 6L, 5L, 6L, 6L, 7L, 6L, 7L, 6L, 7L, 6L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 7L, 7L, 6L, 6L, 7L, 8L, 7L, 9L, 8L, 6L, 7L, 8L, 6L, 9L, 8L, 8L, 9L, 8L, 7L, 9L, 8L, 8L, 10L, 7L, 9L, 9L, 8L, 8L, 10L, 9L, 8L, 8L, 8L, 9L, 9L, 8L, 10L, 9L, 9L, 9L, 9L, 8L, 9L, 9L, 9L, 8L, 9L, 8L, 8L, 9L, 10L, 9L, 8L, 9L, 9L, 10L, 9L, 9L, 8L, 8L, 8L, 9L, 8L, 8L, 10L, 9L, 10L, 10L, 10L, 9L, 11L, 11L, 10L, 8L, 9L, 9L, 14L, 10L, 9L, 12L, 9L, 10L, 12L, 9L, 11L, 12L, 10L, 10L, 8L, 12L, 11L, 11L, 11L, 11L, 10L, 11L, 6L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 7L, 5L, 7L, 6L, 6L, 5L, 6L, 8L, 4L, 6L, 7L, 7L, 7L, 5L, 7L, 6L, 6L, 8L, 7L, 6L, 8L, 9L, 8L, 5L, 7L, 8L, 5L, 5L, 6L, 7L, 6L, 7L, 7L, 8L, 7L, 9L, 9L, 8L, 8L, 8L, 7L, 9L, 8L, 11L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 8L, 7L, 8L, 9L, 9L, 7L, 9L, 7L, 8L, 7L, 9L, 10L, 9L, 10L, 9L, 8L, 9L, 10L, 9L, 9L, 8L, 11L, 9L, 9L, 10L, 9L, 9L, 10L, 9L, 9L, 9L, 10L, 9L, 11L, 8L, 8L, 11L, 8L, 10L, 9L, 10L, 9L, 10L, 10L, 10L, 8L, 9L, 10L, 10L, 10L, 10L, 10L, 8L, 10L, 10L, 12L, 10L, 11L, 10L, 11L, 10L, 11L, 11L, 10L, 11L, 12L, 10L, 10L, 9L, 10L, 10L, 10L, 10L, 12L, 10L, 10L, 12L, 10L, 10L, 9L, 11L, 9L, 12L, 9L, 11L, 12L, 10L, 9L, 9L, 10L, 10L, 15L, 10L, 11L, 10L, 11L, 11L, 11L, 10L, 10L, 13L, 10L, 10L, 10L, 10L, 10L, 10L, 13L, 9L, 11L, 10L, 11L, 12L, 11L, 13L, 12L, 11L, 11L, 12L, 9L, 14L, 14L, 7L, 6L, 7L, 5L, 6L, 7L, 7L, 6L, 5L, 6L, 6L, 5L, 8L, 9L, 8L, 9L, 6L, 7L, 7L, 7L, 6L, 8L, 7L, 9L, 9L, 8L, 8L, 8L, 8L, 9L, 8L, 9L, 8L, 9L, 8L, 9L, 8L, 9L, 9L, 9L, 8L, 8L, 9L, 8L, 9L, 8L, 11L, 9L, 11L, 8L, 9L, 11L, 9L, 10L, 9L, 11L, 10L, 8L, 9L, 10L, 11L, 10L, 9L, 8L, 11L, 11L, 10L, 10L, 10L, 12L, 12L, 11L, 10L, 12L, 13L, 10L, 11L, 5L, 6L, 6L, 7L, 8L, 7L, 6L, 7L, 7L, 6L, 8L, 7L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 9L, 8L, 7L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 7L, 6L, 10L, 8L, 8L, 9L, 8L, 8L, 9L, 10L, 9L, 9L, 9L, 10L, 9L, 10L, 8L, 9L, 8L, 8L, 9L, 8L, 8L, 8L, 11L, 10L, 8L, 10L, 9L, 9L, 10L, 8L, 9L, 10L, 9L, 10L, 10L, 9L, 10L, 9L, 8L, 9L, 13L, 8L, 10L, 9L, 10L, 10L, 10L, 11L, 8L, 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0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 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0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L)), estimator = function (formula, data, r = 1/1000, logn = log(1000), relat = c("cos", "sigmoid", "tanh", "atan", "sin", "erf")) { fparam <- NULL fmla.proc <- as.character(formula)[2:3] fobserved <- fmla.proc[1] fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = " ", replacement = "") fmla.proc[2] <- stringi::stri_replace_all(str = fmla.proc[2], fixed = "\n", replacement = "") fparam <- stringi::stri_split_fixed(str = fmla.proc[2], pattern = "+I", omit_empty = FALSE)[[1]] sj <- (stringi::stri_count_fixed(str = fparam, pattern = "*")) sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = "+")) for (rel in relat) { sj <- sj + (stringi::stri_count_fixed(str = fparam, pattern = rel)) } tryCatch(utils::capture.output({ out <- stats::glm(formula = formula, data = data, family = stats::gaussian) mlik <- (-(stats::BIC(out) - 2 * log(r) * sum(sj)) + 1000)/2 waic <- (out$deviance + 2 * out$rank) + 10000 dic <- (out$deviance + logn * out$rank) + 10000 summary.fixed <- list(mean = stats::coefficients(out)) }, error = function(err) { print(err) mlik <- -10000 waic <- 10000 dic <- 10000 summary.fixed <- list(mean = array(0, dim = length(fparam))) })) return(list(mlik = mlik, waic = waic, dic = dic, summary.fixed = summary.fixed)) }, estimator.args = list(data = list(Age = c(15L, 7L, 9L, 10L, 7L, 8L, 20L, 16L, 9L, 14L, 10L, 11L, 10L, 12L, 7L, 10L, 7L, 9L, 11L, 10L, 12L, 9L, 10L, 11L, 11L, 15L, 11L, 10L, 18L, 19L, 8L, 16L, 8L, 11L, 9L, 9L, 5L, 5L, 4L, 7L, 9L, 7L, 6L, 8L, 7L, 10L, 10L, 7L, 8L, 8L, 8L, 4L, 7L, 7L, 9L, 10L, 8L, 8L, 10L, 6L, 13L, 8L, 20L, 11L, 13L, 15L, 9L, 14L, 9L, 12L, 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0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L), Femele = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 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list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list( max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE))
30: do.call(runemjmcmc, vect[1:(length(vect) - 5)])
31: doTryCatch(return(expr), name, parentenv, handler)
32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: tryCatchList(expr, classes, parentenv, handlers)
34: tryCatch({ set.seed(as.integer(vect$cpu)) do.call(runemjmcmc, vect[1:(length(vect) - 5)]) vals = hash::values(hashStat) fparam = mySearch$fparam cterm = max(vals[1, ], na.rm = TRUE) ppp = mySearch$post_proceed_results_hash(hashStat = hashStat) post.populi = sum(exp(hash::values(hashStat)[1, ][1:vect$NM] - cterm), na.rm = TRUE) betas = NULL mliks = NULL if (vect$save.beta) { Nvars = mySearch$Nvars linx = mySearch$Nvars + 4 lHash = length(hashStat) mliks = hash::values(hashStat)[which((1:(lHash * linx))%%linx == 1)] betas = hash::values(hashStat)[which((1:(lHash * linx))%%linx == 4)] for (i in 1:(Nvars - 1)) { betas = cbind(betas, hash::values(hashStat)[which((1:(lHash * linx))%%linx == (4 + i))]) } betas = cbind(betas, hash::values(hashStat)[which((1:(lHash * linx))%%linx == (0))]) } preds = NULL if (vect$predict) { preds = mySearch$forecast.matrix.na.fast(link.g = vect$link, covariates = (vect$test), betas = betas, mliks.in = mliks)$forecast } ret = list(post.populi = post.populi, p.post = ppp$p.post, cterm = cterm, preds = preds, fparam = fparam, betas = betas, mliks = mliks) if (length(cterm) == 0) { vect$cpu = as.integer(vect$cpu) + as.integer(stats::runif(1, 1, 10000)) if (vect$cpu < 50000) ret = runpar.infer(vect) else ret = NULL }}, error = function(err) { print(paste0("error in thread ", vect[length(vect)])) print(err) vect$cpu = as.integer(vect$cpu) + as.integer(stats::runif(1, 1, 10000)) if (vect$cpu < 50000) ret = runpar.infer(vect) else ret = err}, finally = { return(ret)})
35: FUN(X[[i]], ...)
36: lapply(X = S, FUN = FUN, ...)
37: doTryCatch(return(expr), name, parentenv, handler)
38: tryCatchOne(expr, names, parentenv, handlers[[1L]])
39: tryCatchList(expr, classes, parentenv, handlers)
40: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
41: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
42: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
43: FUN(X[[i]], ...)
44: lapply(seq_len(cores), inner.do)
45: parallel::mclapply(X = X, FUN = FUN, mc.preschedule = TRUE, mc.cores = mc.cores)
46: mcgmj(X = params, FUN = runpar.infer, mc.cores = n.cores)
47: pinferunemjmcmc(n.cores = M, report.level = 0.2, num.mod.best = NM, simplify = TRUE, predict = TRUE, test.data = as.data.frame(test), link.function = g, runemjmcmc.params = list(formula = formula1, data = data.example, estimator = estimate.gamma.cpen, estimator.args = list(data = data.example), recalc_margin = 1000, save.beta = TRUE, interact = TRUE, outgraphs = FALSE, relations = c("to25", "expi", "logi", "to35", "troot", "sigmoid"), relations.prob = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1), interact.param = list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list(max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE)))
48: withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))
49: suppressMessages(pinferunemjmcmc(n.cores = M, report.level = 0.2, num.mod.best = NM, simplify = TRUE, predict = TRUE, test.data = as.data.frame(test), link.function = g, runemjmcmc.params = list(formula = formula1, data = data.example, estimator = estimate.gamma.cpen, estimator.args = list(data = data.example), recalc_margin = 1000, save.beta = TRUE, interact = TRUE, outgraphs = FALSE, relations = c("to25", "expi", "logi", "to35", "troot", "sigmoid"), relations.prob = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1), interact.param = list(allow_offsprings = 3, mutation_rate = 250, last.mutation = 100, max.tree.size = 5, Nvars.max = 15, p.allow.replace = 0.9, p.allow.tree = 0.01, p.nor = 0.9, p.and = 0.9), n.models = 500, unique = TRUE, max.cpu = 1L, max.cpu.glob = 1L, create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE, burn.in = 100, print.freq = 0L, advanced.param = list(max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE))))
50: eval(code, test_env)
51: eval(code, test_env)
52: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)
53: doTryCatch(return(expr), name, parentenv, handler)
54: tryCatchOne(expr, names, parentenv, handlers[[1L]])
55: tryCatchList(expr, classes, parentenv, handlers)
56: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)
57: doWithOneRestart(return(expr), restart)
58: withOneRestart(expr, restarts[[1L]])
59: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })
60: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())
61: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)
62: FUN(X[[i]], ...)
63: lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call)
64: doTryCatch(return(expr), name, parentenv, handler)
65: tryCatchOne(expr, names, parentenv, handlers[[1L]])
66: tryCatchList(expr, classes, parentenv, handlers)
67: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})
68: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, shuffle = shuffle, error_call = error_call))
69: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, shuffle = shuffle, error_call = error_call)
70: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel, shuffle = shuffle)
71: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed")
72: test_check("EMJMCMC")
An irrecoverable exception occurred. R is aborting now ...
Saving _problems/test-abalone-BGNLM-115.R
[ FAIL 1 | WARN 2 | SKIP 3 | PASS 83 ]
══ Skipped tests (3) ═══════════════════════════════════════════════════════════
• empty test (3): , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-abalone-BGNLM.R:115:3'): pinferunemjmcmc outputs with correct elements ──
Error in `expect_gte(ncol(res1_par[["feat.stat"]]), 2L)`: Result of comparison must be `TRUE`, `FALSE`, or `NA`
Backtrace:
▆
1. └─testthat::expect_gte(ncol(res1_par[["feat.stat"]]), 2L) at test-abalone-BGNLM.R:115:3
2. └─testthat:::expect_compare_(">=", act, exp)
3. └─cli::cli_abort(...)
4. └─rlang::abort(...)
[ FAIL 1 | WARN 2 | SKIP 3 | PASS 83 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-macos-arm64