■_
rubykaigi2014 みっかめ
burn の作者日本人だったのかー github も見に行ってたのに気がつかなかった
bash の parse.y を読んでみようかと思ったw bash の実装の解説については こういうのが。ちょっと前にこれの読書会やってましたね The Architecture of Open Source Applications: The Bourne-Again Shell
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rubykaigi2014 みっかめ
burn の作者日本人だったのかー github も見に行ってたのに気がつかなかった
bash の parse.y を読んでみようかと思ったw bash の実装の解説については こういうのが。ちょっと前にこれの読書会やってましたね The Architecture of Open Source Applications: The Bourne-Again ShellRubyKaigi2014ふつかめ。 あるセッション中、斜め後ろの席でずーーーーーーーっと鼻をすすってるのがいた。 ノベルティでポケットティッシュがありゃ良かったのにと思ったり思わなかったり。
読みかけの論文がいくつか。 結構面白いんだけどはたして読み切ってここに何か書くところまでいけるのか。
RubyKaigi2014 いちにちめ
こういうのがリツイートで回ってきたんだけど、 はて実装のどの辺りがどう影響しているんだろう。
シェルで単純な”列”の抜き出しを行う場合、行が少ないうちはawkとcutで実行時間は拮抗しているが、行が多くなるほどawkの方が早くなる。
(少なくともGNUの実装では)
#あまり知られていない事実を晒せ
#シェル芸
— 田窪守雄 (@takubo_morio) 2014, 9月 17
なんか一部でこの記事が話題になってたけど
Pythonへの型アノテーション導入を目指すGuido van Rossum氏
原文(投稿日:2014/08/15)へのリンク
元記事は一か月も前のなんだがねえ…
Road to Rust 1.0 What is left to do We’ve made great progress, but there is still a lot to do before the release. Here is a list of the big-ticket changes we are currently working on:
で挙げられている項目が
歴史小説における創作を歴史的事実であるとして(思い込んで?) 妙ちきりんな「教訓」らしきものをひねり出して 「お説教」かます手合いが大嫌いなんですがどうしてくれよう…
RubyKaigi の週ですやね。
THE BLUE HEARTS が好きでして。 北米某所に長期出張させられたときは手持ちのCD全部持って行ったなー (当時は iなんとかとかDAPの類は影も形もなかった(はず))。 THE BLUE HEARTS/歌詞:TRAIN-TRAIN/うたまっぷ歌詞無料検索 ▶ ブルーハーツTRAIN-TRAIN - YouTube この曲(の歌詞は)はよく「弱いものたちが夕暮れさらに弱いものを叩く~」のあたり(だけ)が引用される気がするけど、 そこだけー? とちとぐんにょりする。
SRGM の話つづき。 論文の内容を確認(追試)できるようなデータないすかねー。 某社の? 使えるようなのあるかなあ… さすがに今はバグデータベースもあるんだけどもごにょごにょ
ふとこの辺を見てみたら気になるタイトルがいくつか。 Early Release - O'Reilly Media Upcoming - O'Reilly Media
なんだこれw If Hemingway Wrote JavaScript - O'Reilly Media
If Hemingway Wrote JavaScript - O'Reilly Media What if William Shakespeare were asked to generate the fibonacci series or Jack Kerouac had to write a factorial program? In If Hemingway Wrote JavaScript, prose and poetry meet code as author Angus Croll explores what it would look like if wordsmiths were to write short JavaScript programs.
これ、もう第2版に? 21st Century C, 2nd Edition - O'Reilly Media
コマンドラインで? というのが気になった Data Science at the Command Line - O'Reilly Media
Data Science at the Command Line - O'Reilly Media Even if you’re already comfortable processing data with R or Python, being able to integrate the command line into your existing workflow will make you a more efficient and productive data scientist. Learn essential concepts and built-in commands of the *nix command line Get started with your own Data Science Toolbox on either Linux, Mac OS X, or Microsoft Windows Use classic command-line tools such as grep, sed, and awk Obtain data from websites, APIs, databases, and spreadsheets Parallelize and distribute data-intensive pipelines to remote machines, including AWS EC2 Clean data in CSV, JSON, and XML/HTML formats using csvkit, and jq, and scrape Apply dimensionality reduction, clustering, regression, and classification algorithms Visualize data and results from the command line using gnuplot and ggplot Turn Bash one-liners and existing Python and R code into reusable command-line tools
(日本でも)こーゆーの好きそうな人いるだろうなあ。 あの人とかあの人とか。
お、Cython 本だ。 Cython - O'Reilly Media
忘れた頃に SRGM の話題。 List of software reliability models - Wikipedia, the free encyclopedia
List of software reliability models - Wikipedia, the free encyclopedia Software reliability is the probability of the software components of producing incorrect output. Software should not wear out and continue to operate after a bad result. Over 225 models have been developed since early 1970s, but how to quantify software reliability still remains unsolved. There is no single model which can be used in every situation. There is no model which is either complete or fully developed.
Over 225 models って…
List of software reliability models - Wikipedia, the free encyclopedia Other Models in software : Jelinski-Moranda Goel-Okumoto (exponential)[1] Rayleigh Titan Reliability Modeling Software (Predictive) Weibull Delayed s-Shaped Inflexion s-Shaped
この中の Titan Reliability~ ってのは覚えにないな。どんなんだろ。
Tim Cook: Steve Jobs' DNA Will Always Be the Foundation of Apple - Mac Rumors
Tim Cook: Steve Jobs' DNA Will Always Be the Foundation of Apple - Mac Rumors His thinking and his taste and his incredible perfectionist kind of view, and his view that you should always innovate, all of those things are alive and well in the company. And I think they always will be. I think his DNA will always be the foundation of Apple.
向こうでもこういう「DNA」の使い方するんだねえ。 日本に特有のものかと思ってた。
ってなんじゃろか。
それはさておき
Ocean of Awareness Parsing: a timeline 1960: The ALGOL 60 spec comes out. It specifies, for the first time, a block structured language. The ALGOL committee is well aware that nobody knows how to parse such a language. But they believe that, if they specify a block-structured language, a parser for it will be invented. Risky as this approach is, it pays off ... 1961: Ned Irons publishes his ALGOL parser. In fact, the Irons parser is the first parser of any kind to be described in print. Ned's algorithm is a left parser -- a form of recursive descent. Unlike modern recursive descent, the Irons algorithm is general and syntax-driven. "General" means it can parse anything written in BNF. "Syntax-driven" (aka declarative) means that parser is actually created from the BNF -- the parser does not need to be hand-written. (略) 1987: Larry Wall introduces Perl 1. Perl embraces complexity like no previous language. Larry uses LALR very aggressively -- to my knowledge more aggressively than anyone before or since. (略) 2000: Larry Wall decides on a radical reimplementation of Perl -- Perl 6. Larry does not even consider using LALR again. (略) 2000 to today: With the retreat from LALR comes a collapse in the prestige of parsing theory. After a half century, we seem to be back where we started. If you took Ned Iron's original 1961 algorithm, changed the names and dates, and translated from code from the mix of assembler and ALGOL into Haskell, you would easily republish it today, and bill it as as revolutionary and new. Marpa Over the years, I had come back to Earley's algorithm again and again. Around 2010, I realized that the original, long-abandoned vision -- an efficient, practical, general and syntax-driven parser -- was now, in fact, quite possible. The necessary pieces had fallen into place. 以下略
eban さんの
「1から100の整数ただし5つの異なる方法」 - jarp,
これ。
% repeat 1000 { echo $[RANDOM%100+1] } | sort -nu | head -100
(範囲が1から100までの整数である)一様乱数であるとして、1000個で抜けが発生する確率はどのくらいなんだろうか (0じゃないよね?) と気になったが、計算は面倒くさそうなので考えるのを止めた。
> floor(runif(1000,min=1,max=100)) [1] 51 48 49 43 46 56 79 5 68 56 11 97 55 70 86 32 33 13 94 84 58 94 [23] 88 93 72 74 38 97 87 3 63 79 33 64 40 30 22 23 43 22 6 39 47 72 [45] 2 59 77 84 82 74 41 72 55 73 93 74 19 40 79 65 31 96 21 2 86 16 [67] 33 50 26 26 66 13 22 41 12 21 93 13 49 31 26 23 62 40 96 61 66 57 [89] 24 89 87 99 56 58 17 94 85 19 27 58 84 7 6 85 20 11 60 97 3 77 [111] 50 66 8 76 4 64 11 48 86 38 40 19 57 20 10 2 50 63 93 59 34 92 [133] 20 44 89 80 98 94 42 24 86 25 61 49 6 23 50 25 13 86 13 69 25 9 [155] 19 1 66 16 27 81 96 18 96 67 95 97 24 66 19 76 40 65 75 5 87 69 [177] 51 65 92 51 16 95 41 19 58 52 48 61 14 66 86 72 89 60 73 39 70 70 [199] 8 85 5 82 49 64 93 36 72 4 94 82 35 16 86 3 37 8 18 86 25 86 [221] 29 4 11 2 8 53 90 24 98 12 66 5 71 42 13 99 83 29 14 8 77 31 [243] 9 50 14 3 28 68 94 7 18 19 51 3 28 28 55 69 70 79 21 7 96 70 [265] 67 14 98 74 40 79 97 89 59 79 48 32 8 14 2 76 50 91 76 13 98 78 [287] 32 1 53 71 41 13 24 72 63 75 36 64 4 61 38 73 24 61 89 66 39 74 [309] 57 9 73 89 65 30 91 63 2 85 39 37 46 85 84 58 48 46 40 99 53 69 [331] 95 71 67 78 74 24 9 51 81 37 88 54 68 10 30 90 54 93 98 58 13 23 [353] 53 17 4 56 99 11 56 83 44 50 96 52 77 71 1 57 94 44 94 79 97 93 [375] 71 31 42 18 44 40 91 59 91 28 18 55 78 35 25 24 66 21 45 52 33 56 [397] 3 41 53 95 55 46 85 99 16 56 4 61 10 24 14 28 1 87 61 24 41 66 [419] 4 4 85 61 77 11 38 46 18 7 13 90 51 39 35 84 15 65 21 41 19 15 [441] 9 66 96 8 55 27 27 83 67 94 97 21 51 38 77 68 82 88 32 88 65 46 [463] 39 66 94 94 85 46 10 86 8 30 74 7 15 51 77 53 92 31 15 2 64 15 [485] 3 13 11 85 29 72 96 52 94 84 35 51 4 54 22 53 48 42 24 85 20 77 [507] 27 80 23 1 9 20 81 98 86 22 95 67 91 37 88 26 22 72 5 67 41 49 [529] 98 17 15 29 70 93 39 65 36 33 76 25 59 62 78 22 98 5 95 71 79 75 [551] 78 54 76 45 77 21 84 20 34 40 74 86 69 97 45 94 63 80 8 45 53 66 [573] 53 14 74 86 11 47 69 64 22 51 47 4 12 98 27 27 64 32 36 52 72 21 [595] 41 43 50 62 16 58 46 82 2 44 22 44 46 59 68 99 63 68 88 16 93 99 [617] 21 11 36 53 61 10 48 88 89 89 72 72 81 16 58 20 24 9 56 20 53 55 [639] 4 2 73 35 26 7 60 14 43 72 65 97 4 37 27 20 37 77 59 95 53 9 [661] 19 3 89 91 29 18 31 22 45 43 16 35 28 50 36 89 43 26 90 54 43 32 [683] 68 82 77 83 49 52 75 35 72 7 59 78 52 58 65 10 74 14 59 55 52 94 [705] 13 82 92 17 1 95 36 1 67 54 39 4 95 46 67 61 35 15 1 46 92 49 [727] 82 21 76 60 39 38 9 22 6 87 82 11 3 1 98 10 36 2 20 42 99 91 [749] 46 92 70 5 76 36 75 90 21 96 40 30 64 65 65 14 44 75 2 27 27 57 [771] 75 83 41 69 10 55 35 92 75 14 34 90 86 6 14 97 6 37 14 8 98 46 [793] 57 44 48 82 39 8 13 26 7 42 3 21 7 97 13 8 59 95 55 17 7 37 [815] 43 92 76 86 77 28 11 79 74 55 41 88 48 30 17 45 83 25 12 45 80 80 [837] 31 2 82 93 29 91 66 15 56 97 66 21 72 40 88 7 73 22 28 15 67 55 [859] 21 49 55 30 36 15 12 85 62 24 40 34 13 75 27 84 83 50 60 60 98 14 [881] 22 37 6 4 20 79 36 97 79 13 62 14 36 43 71 71 56 67 67 52 70 69 [903] 36 47 71 81 70 69 28 98 93 48 36 14 75 43 37 24 45 21 95 42 41 11 [925] 78 96 79 93 19 87 93 10 59 31 3 14 7 3 93 61 14 11 28 24 49 72 [947] 97 73 76 84 69 53 96 3 95 97 75 31 84 19 79 81 12 44 70 17 23 55 [969] 13 61 57 5 5 55 73 43 12 93 99 32 30 74 21 64 45 99 66 24 91 14 [991] 68 81 86 4 53 44 67 25 12 85 > unique(sort(floor(runif(1000,min=1,max=100)))) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 [24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 [47] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 [70] 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 [93] 93 94 95 96 97 98 99 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100 > length(unique(sort(floor(runif(1000,min=1,max=101))))) [1] 100
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