unique_ptrã¨ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹é…列 - Qiita
生ãƒã‚¤ãƒ³ã‚¿ã‚’C++ã§ä½¿ã†ã®ãŒä¸å®‰ã§ä»•æ–¹ãªã„ã®ã§ã‚¹ãƒžãƒ¼ãƒˆãƒã‚¤ãƒ³ã‚¿ã‚’勉強ã—ã¦ã„ã‚‹ã®ã ãŒã€unique_ptr周りã®æŒ™å‹•ã§ç‹¬ç‰¹ãªéƒ¨åˆ†ãŒã‚ã£ãŸã®ã§ã¾ã¨ã‚ã¦ãŠã“ã†ã¨æ€ã†ã€‚ ãƒãƒªãƒ¢ãƒ¼ãƒ•ã‚£ãƒƒã‚¯ãªå®šç¾©ã‚’ã™ã‚‹ã¨ã C++ã§ãƒãƒªãƒ¢ãƒ¼ãƒ•ã‚£ãƒƒã‚¯ãªã‚³ãƒ¼ãƒ‰ã‚’書ãã¨ãã¯ã€æ¬¡ã®ã‚ˆã†ã«è¦ªã‚¯ãƒ©ã‚¹ã®ãƒã‚¤ãƒ³ã‚¿ã«åクラスをnewã§ä»£å…¥ã™ã‚‹ã“ã¨ãŒå¤šã„。 ãŸã ã—誰もãŒçŸ¥ã‚‹ã‚ˆã†ã«å¤§åŽŸå‰‡ã¨ã—ã¦newã—ãŸã‚‰å¿…ãšdeleteã—ãªã‘ã‚Œã°ãªã‚‰ãªã„ã®ã§ã€ã“ã®æ–¹æ³•ã§ã¯ã†ã£ã‹ã‚Šdeleteã—忘れるä¸å®‰ãŒã¤ã„ã¦å›žã‚‹ã€‚ ãã“ã§ãƒã‚¤ãƒ³ã‚¿ã®ä»£ã‚ã‚Šã«ã‚¹ãƒžãƒ¼ãƒˆãƒã‚¤ãƒ³ã‚¿ã‚’使ã†ã€‚ã“ã‚Œã¯C++11以é™å°Žå…¥ã•ã‚ŒãŸæ©Ÿèƒ½ã§ã€ã‚¹ã‚³ãƒ¼ãƒ—を抜ã‘ã‚‹ã¨ãå‹æ‰‹ã«ãƒ‡ã‚¹ãƒˆãƒ©ã‚¯ã‚¿ã‚’呼んã§ãれる便利ãªä»£ç‰©ã 。使ã†ã«ã¯memoryライブラリをincludeã—ã¦ãŠãã€ã•ã‚‰ã«ã‚³ãƒ³ãƒ‘イルオプションã«-std=c++11（c++11以é™ãªã‚‰ã‚ˆã„）ã¨æŒ‡å®šã™ã‚‹å¿…è¦ãŒã‚る。
C++高速化
ã¯ã˜ã‚㫠速ã„プãƒã‚°ãƒ©ãƒ ã§å¾—られるメリットを超ãˆã‚‹ã‚ˆã†ãªã‚³ã‚¹ãƒˆã‚’払ã‚ãªã„よã†ã«ã€‚ ã¾ãšã¯å‹•ãプãƒã‚°ãƒ©ãƒ を書ã„ã¦ç›®çš„ã‚’é”æˆã™ã‚‹ã“ã¨ãŒå¤§äº‹ã€‚ 自分律速ã˜ã‚ƒãªãã¦ãƒ—ãƒã‚°ãƒ©ãƒ 律速ã ãªã¨æ„Ÿã˜ãŸæ®µéšŽã§ãƒªãƒ•ã‚¡ã‚¯ã‚¿ãƒªãƒ³ã‚°ã‚’考ãˆã‚‹ã€‚ プãƒã‚°ãƒ©ãƒ 本æ¥ã®æ„図ãŒèªã¿å–ã‚Œãªããªã‚Šãã†ãªãƒžãƒ‹ã‚¢ãƒƒã‚¯ãªé«˜é€ŸåŒ–ã¯é¿ã‘る。 清ãæ£ã—ãメンテナンスã—ã‚„ã™ã„プãƒã‚°ãƒ©ãƒ を書ãã»ã†ãŒçµå±€ã¯ç”Ÿç”£çš„。 å¦ç¿’目的ã§ãªã„é™ã‚Šã€è»Šè¼ªã®å†ç™ºæ˜Žã‚’é¿ã‘る。 ã‚„ã‚ã†ã¨ã—ã¦ã„ã‚‹ã“ã¨ã¯ãã£ã¨æ—¢ã«èª°ã‹ãŒå®Ÿç¾ã—〠å†åˆ©ç”¨å¯èƒ½ãªå½¢ã§å…¬é–‹ã—ã¦ãã‚Œã¦ã„ã‚‹ã¯ãšã€‚ ã¾ãšã¯æ¨™æº–ライブラリã¨ã‹Boostを探ã—ã¦ã¿ã‚‹ã€‚ ã‚ã¨GitHubã§ã‚¹ã‚¿ãƒ¼ãŒå¤šã„ã‚„ã¤ã¨ã‹ã€‚ é ‘å¼µã‚Œã‚³ãƒ³ãƒ‘ã‚¤ãƒ© Intelã® icc ã§ãƒ“ルドã•ã‚ŒãŸãƒ—ãƒã‚°ãƒ©ãƒ ã¯é€Ÿã„らã—ã„。 gcc ã‚„ clang ã®æœ€é©åŒ–技術もç€ã€…ã¨é€²æ©ã—ã¦ãŠã‚Šã€ æ–°ã—ã„コンパイラを使ã†ã»ã†ãŒãã®æ©æµã‚’å—ã‘られる。 最é©åŒ–オプション htt
cereal Docs - Main
cereal - A C++11 library for serialization cereal is a header-only C++11 serialization library. cereal takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, XML, or JSON. cereal was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. cer
Rcpp使ã£ã¦ãƒ‘フォーマンスを測ã£ãŸã‚‰ã«åˆ†é¡žã•ã‚Œã‚‹â€¦ï¼Ÿ - My Life as a Mock Quant
ã“ã†ã„ã†ã©ã†ã§ã‚‚ã„ã„関数ãŒã‚ã£ãŸã¨ã™ã‚‹ã€‚ hoge <- function(x) { result = x + 1; for(i in 1:10000000){ result <- result + 1; } result } ã“ã®é–¢æ•°ã®å®Ÿè¡Œé€Ÿåº¦+パフォーマンスを測るã«ã¯Rprof関数を使ã£ã¦ä»¥ä¸‹ã®ã‚ˆã†ã«æ›¸ã。 > Rprof(tmp <- tempfile()) > a <- hoge(1:10) > Rprof() > summaryRprof(tmp) $by.self self.time self.pct total.time total.pct "hoge" 2.76 61.88 4.46 100.00 "+" 0.88 19.73 0.88 19.73 ":" 0.82 18.39 0.82 18.39 $by.total total.time total.pct self.t
Rcppã§å®šç¾©ã™ã‚‹C++ã®é–¢æ•°ã«é–¢æ•°ã‚’渡ã™ã¨ãã¯Rcpp::Funcã§OK - My Life as a Mock Quant
ã“ã®è¾º Calling R Functions from C++ http://dirk.eddelbuettel.com/papers/rcpp_intro_genentech_2015-01.pdf ã®ä¾‹ã§ã¯ï¼‘引数ã—ã‹æ¸¡ã•ãªã„ケースã°ã‹ã‚Šã ã£ãŸã®ã§ã€è¤‡æ•°å¼•æ•°ã®é–¢æ•°ã‚’渡ã›ã‚‹ã“ã¨ã«æ°—ãŒã¤ã‹ãªã‹ã£ãŸã€‚以下ã§ã¯é©å½“ãªé–¢æ•°(someFunc)ã¨ã€Rã‹ã‚‰é–¢æ•°ã‚’引数ã«å–ã£ã¦å‡¦ç†ã‚’è¡Œã†é–¢æ•°callFuncを定義ã—ã¦ã„る。 library(Rcpp) sourceCpp(code=' #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] NumericVector someFunc(NumericVector x, double t, CharacterVector c) { return (x+t); } // [[Rcpp::expo
Faster Multivariate Normal densities with RcppArmadillo and OpenMP
Faster Multivariate Normal densities with RcppArmadillo and OpenMP Nino Hardt, Dicko Ahmadou, Benjamin Christoffersen — written Jul 13, 2013 — updated Feb 2, 2020 — source The Multivariate Normal density function is used frequently for a number of problems. Especially for MCMC problems, fast evaluation is important. Multivariate Normal Likelihoods, Priors and mixtures of Multivariate Normals requi
Generating a multivariate gaussian distribution using RcppArmadillo
Generating a multivariate gaussian distribution using RcppArmadillo Ahmadou Dicko — written Mar 12, 2013 — source There are many ways to simulate a multivariate gaussian distribution assuming that you can simulate from independent univariate normal distributions. One of the most popular method is based on the Cholesky decomposition. Let’s see how Rcpp and Armadillo perform on this task. #include <
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