投稿時間:2021-06-01 02:23:43 RSSフィード2021-06-01 02:00 分まとめ(27件)

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IT	気になる、記になる…	新型｢MacBook Pro｣向けミニLEDディスプレイの部品の出荷は今年第3四半期に開始か	https://taisy0.com/2021/06/01/141169.html	globall	2021-05-31 16:07:13
js	JavaScriptタグが付けられた新着投稿 - Qiita	jQueryライブラリを使わないスライドショー	https://qiita.com/taroccoli/items/9cb1cabbc1fc63990e30	jQueryライブラリを使わないスライドショーこれまでスライドショーといえば業務で何度もslick等のライブラリにお世話になってきましたが、ネイティブJavaScriptとCSSで実装にチャレンジです。	2021-06-01 01:45:01
js	JavaScriptタグが付けられた新着投稿 - Qiita	フロントエンドの刺激的なコーディング課題6選	https://qiita.com/baby-degu/items/817bf0ca30b23ff205ac	ダッシュボードを複製できるか試してみてくださいAybükeCeylanによる「採用担当ダッシュボードUI」学べることJavaScriptとCSSで美しいデータチャートを作成する方法。	2021-06-01 01:18:31
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	なんかWSL2がインターネットにつながらなくなったときの解決方法	https://qiita.com/kotauchisunsun/items/71fae973afa00ebb871a	これで、WSLに割り当てられているIPアドレスと、dockerのIPアドレスが競合しないようにすることで、ネットワークが動くように調整しています。	2021-06-01 01:34:57
Docker	dockerタグが付けられた新着投稿 - Qiita	なんかWSL2がインターネットにつながらなくなったときの解決方法	https://qiita.com/kotauchisunsun/items/71fae973afa00ebb871a	これで、WSLに割り当てられているIPアドレスと、dockerのIPアドレスが競合しないようにすることで、ネットワークが動くように調整しています。	2021-06-01 01:34:57
技術ブログ	Developers.IO	バックエンドが共有PaaSでDNSもオリジンも手を付けられない状況でCloudflare Waiting Roomを利用する	https://dev.classmethod.jp/articles/work-around-for-cloudfare-waiting-room/	cloudflare	2021-05-31 16:22:53
海外TECH	DEV Community	Visualizing Algorithm Runtimes in Python	https://dev.to/chroline/visualizing-algorithm-runtimes-in-python-f92	Visualizing Algorithm Runtimes in PythonThis article will cover how you can use visualization libraries and software to determine runtime complexities for different algorithms We will cover the basic usage of matplotlib for visualization of d plots and numpy for calculating lines of best fit and then go over how these libraries can be used to determine their runtime complexity either through guesstimation or by comparing the plots of their runtimes to that of known functions i e y x y n If you are interested in downloading the code featured in this article please visit this repository on my GitHub chroline visualizingRuntimes What is runtime complexity Runtime complexity more specifically runtime complexity analysis is a measurement of how “fast an algorithm can run as the amount of operations it requires increases Before we dive into visualizing the runtimes of different algorithms let s look at a couple of basic examples to explain the concept Take the following add function It accepts two parameters a and b and performs addition on a and b def add a b return a bWhen given any two parameters and and and the amount of operations does not change Therefore we say the algorithm runs in constant or O n time However now consider the following permutations function It accepts one main parameter string and prints all of the permutations of that string def permutations string step if step len string print join string for i in range step len string string copy c for c in string string copy step string copy i string copy i string copy step permutations string copy step As you could imagine this function would take much longer than the previous add function in fact this function would run in what is called factorial time represented as O n That is because as the amount of characters in string increases the amount of operations required to find all the permutations increases factorially When comparing the runtimes of two functions visually you would notice a stark contrast in the graphs they produce For the add function you would observe a flat line as the input of the function does not affect the amount of operations required by the function However for the permutations function you would observe a line that drastically spikes upwards the slope of the line would approach infinity because the amount of operations increases factorially as the size of the input increases Now that we have covered the basics of runtime complexity analysis we can begin the process of writing code to visualize the runtimes of different algorithms InitializationBefore running any visualizations we must first import the necessary libraries and initialize them matplotlib is a library that will create and display the graphsnumpy is a library that consists of numerous mathematical utility functionstimeit is a library that we will use to time how long each call to the algorithm takesmath is the basic Python math libraryrandom is the basic Python randomization libraryimport matplotlib pyplot as pltimport numpy as npimport timeitimport mathimport randomplt rcParams figure figsize set size of plot DemosBelow are some code segments that demonstrate how to use matplotlib and numpy sum functionThe Python sum function calculates the sum of all elements of a provided Iterable This function implements an algorithm with a O n  runtime complexity To test this we will use the linspace method from the numpy library to generate an iterable with evenly spaced values ranging from to The graph although not a perfectly straight plot illustrates this ns np linspace dtype int ts timeit timeit sum range format n number for n in ns plt plot ns ts or We can add a line of best fit using a th degree function to further exemplify this The graph will never be a perfectly straight line but it should come close degree   coeffs   np polyfit ns  ts  degree p   np polyd coeffs plt plot ns  ts or plt plot ns   p n for nin ns b List IndexingRetrieving an item from a list list indexing runs with O  runtime complexity which means that the amount of items in the list does not affect how long the algorithm takes to run How is this represented in a graph lst   list range ns   np linspace  len lst    endpoint False  dtype int ts    timeit timeit    lst format n                     globals globals                     number for nin ns plt plot ns  ts or degree   coeffs   np polyfit ns  ts  degree p   np polyd coeffs plt plot ns   p n for nin ns b AlgorithmsNow we will look at the graphs produced by the following algorithms linear searchbinary searchinsertion sort Linear SearchLinear search has a runtime complexity of O n which will be represented by an approximately straight line Red plots demonstrate searching for an element in a shuffled  blue plots demonstrate searching for an element that is not present in the list The line of best fit for the red plots will generally be lesser than that of the blue plots because searching for an element that is not present in the list requires iterating through the entire list searches for an item in a listdef contains lst x for y in lst if x y return True return Falsens np linspace dtype int red plotsts timeit timeit contains lst setup lst list range random shuffle lst format n globals globals number for n in ns plt plot ns ts or line of best fit for red plotsdegree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns r blue plotsts timeit timeit contains lst setup lst list range format n globals globals number for n in ns plt plot ns ts ob line of best fit for blue plotsdegree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns b Binary SearchBinary search runs with O log n  runtime complexity searches for an item in a list using linear searchdef contains lst x lo hi len lst while lo lt hi mid lo hi if x lt lst mid hi mid elif x gt lst mid lo mid else return True else return Falsens np linspace dtype int ts timeit timeit contains lst setup lst list range format n globals globals number for n in ns plt plot ns ts or degree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns b Above is what the graph should look like in a near perfect simulation As you can see there are some some outliers and in a perfect simulation the line of best fit would be identical to a logarithmic function Insertion SortWhat runtime complexity does insertion sort have We can use the plots of its runtime and compare those plots against the graphs of known runtimes to determine which is the closest match def insertion sort lst for i in range len lst for j in range i if lst j gt lst j lst j lst j lst j lst j else break valuesns np linspace dtype int ts timeit timeit insertion sort lst setup lst list range random shuffle lst format n globals globals number for n in ns plt plot ns ts or degree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns r Now we can compare that graph with graphs of different runtimes to ultimately determine which is most similar and which runtime complexity insertion sort has Red plots represent O log n  blue plots represent O n  green plots represent O n y log x vertically stretched xns range ts math log n for n in ns plt plot ns ts or y x ns range ts n n for n in ns plt plot ns ts ob y x vertically stretched x horizontally compressed xns range ts math pow n for n in ns plt plot ns ts og Based on these graphs it is safe to assume that insertion sort runs in O n time Mystery function runtime analysisCan we use visualization of the runtimes of mystery functions to guesstimate their runtime complexities Mystery function def f l list val l is a python list with n items d for i in range len l d l i i return d val ns range ts timeit timeit f lst format n setup lst list range random shuffle lst format n globals globals number for n in ns plt plot ns ts or degree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns b Without even comparing this graph to the graphs of the possible runtimes we can already safely assume that this function operates in O n runtime Mystery function def g l l is a python list of integers of length n pairs i j for i in range len l for j in range len l if i lt j result for i j in pairs if l i l j result append i j return resultns range ts timeit timeit g lst setup lst list range format n globals globals number for n in ns plt plot ns ts or degree coeffs np polyfit ns ts degree p np polyd coeffs plt plot ns p n for n in ns b This graph looks very similar to the one for insertion sort so we can determine that this function has a runtime complexity of O n Mystery function def h n if n lt return n else return h n h n ns range ts timeit timeit h format n globals globals number for n in ns plt plot ns ts or This function is more ambiguous than the previous two It is evident that its runtime must be greater than O n as the slope increases as n increases Let s consider the graphs of runtimes O n in red and O n in blue y n vertically stretched xns range ts n n for n in ns plt plot ns ts or y n vertically compressed xns range ts math pow n for n in ns plt plot ns ts ob The graph of the runtime of mystery function more closely resembles the blue plots so therefore the runtime complexity of mystery function is O n ConclusionUsing these visualization libraries we are able to determine the runtime complexities of functions and algorithms by comparing them to plots graphs of known runtimes i e comparing plots of insertion sort runtime against y n In addition to determining runtime complexities this methodology can be used to compare the speeds of different algorithms against each other With only a few lines of code you can quickly see the speed at which your chosen algorithms will run with large sets of data ResourcesPyplot tutorial   Matplotlib documentationnumpy polyfit   NumPy v ManualPython Program to Display Fibonacci Sequence Using Recursion programiz com How to find all possible permutations of a given string in Python tutorialspoint com time complexities that every programmer should know Adrian Mejia Blog	2021-05-31 16:16:18
Apple	AppleInsider - Frontpage News	iPhone 12 active user base grew faster than iPhone 11, despite later start	https://appleinsider.com/articles/21/05/31/iphone-12-active-user-base-grew-faster-than-iphone-11-despite-later-start?utm_medium=rss	iPhone active user base grew faster than iPhone despite later startA new report claims that the iPhone range had more active users in Q than the iPhone did at the same point in Apple s iPhone Pro MaxIn line with its previous report that the iPhone was the best selling G smartphone in October Counterpoint Research has now released claims for Q While Apple itself has not released any iPhone sales figures since making it hard to check the assumptions for accuracy Counterpoint estimates that the overall user base has increased by in the last year Read more	2021-05-31 17:00:11
Apple	AppleInsider - Frontpage News	New Apple AirPods deals drive prices down to $119.99	https://appleinsider.com/articles/21/05/31/new-apple-airpods-deals-drive-prices-down-to-11999?utm_medium=rss	New Apple AirPods deals drive prices down to Amazon s latest round of Apple price cuts see discounts on AirPods increase to up to off with prices as low as Apple AirPods saleThe latest AirPods sale at Amazon offers up fresh discounts on AirPods with a Wired Charging Case and AirPods with a Wireless Charging Case knocking to off the popular earphones Read more	2021-05-31 16:20:41
海外TECH	Engadget	LG has reportedly stopped making smartphones	https://www.engadget.com/lg-smartphone-production-ended-183050001.html?src=rss_b2c	business	2021-05-31 16:30:50
海外TECH	Engadget	Google Photos' free unlimited storage ends tomorrow	https://www.engadget.com/google-photos-free-unlimited-storage-end-date-133058166.html?src=rss_b2c	google	2021-05-31 16:15:58
海外TECH	CodeProject Latest Articles	GFX Forever: The Complete Guide to GFX for IoT	https://www.codeproject.com/Articles/5302085/GFX-Forever-The-Complete-Guide-to-GFX-for-IoT	drawing	2021-05-31 16:45:00
海外科学	NYT > Science	Will the Next Space-Weather Season Be Stormy or Fair?	https://www.nytimes.com/2021/05/28/science/astronomy-sun-space-weather.html	friend	2021-05-31 16:29:02
金融	RSS FILE - 日本証券業協会	ＮＩＳＡ及びジュニアＮＩＳＡ口座開設・利用状況調査結果について	https://www.jsda.or.jp/shiryoshitsu/toukei/nisajoukyou.html	調査結果	2021-05-31 17:39:00
金融	金融庁ホームページ	保険募集の基本的ルールの創設、保険募集人に対する規制の整備に係る「規制の事後評価」を公表しました。	https://www.fsa.go.jp/seisaku/r2ria.html	保険募集	2021-05-31 17:00:00
金融	金融庁ホームページ	「マネー・ローンダリング及びテロ資金供与対策に係る態勢整備の期限設定について」を公表しました。	https://www.fsa.go.jp/news/r2/20210531_amlcft/2021_amlcft_yousei.html	資金供与	2021-05-31 17:00:00
金融	金融庁ホームページ	金融庁電子申請・届出システムの利用開始に向けたご連絡について公表しました。	https://www.fsa.go.jp/news/r2/sonota/20210531.html	電子	2021-05-31 17:00:00
金融	金融庁ホームページ	金融庁防災業務計画の改正について（令和３年５月31日）公表しました。	https://www.fsa.go.jp/news/r2/sonota/20210531-2/20210531.html	防災業務計画	2021-05-31 17:00:00
金融	金融庁ホームページ	貸金業関係資料集について更新しました。	https://www.fsa.go.jp/status/kasikin/20210531/index.html	関係	2021-05-31 17:00:00
ニュース	ジェトロ ビジネスニュース（通商弘報）	中銀、2021年のGDP成長率見通しを4.0％に上方修正	https://www.jetro.go.jp/biznews/2021/06/288fcf2e58a98750.html	上方修正	2021-05-31 16:20:00
ニュース	ジェトロ ビジネスニュース（通商弘報）	第1四半期のGDP成長率は前年同期比1.1％、同期以来のプラス成長	https://www.jetro.go.jp/biznews/2021/06/9f920e2a2faeb7c6.html	前年同期	2021-05-31 16:10:00
ニュース	BBC News - Home	In pictures: Bank Holiday Monday brings hottest day of the year	https://www.bbc.co.uk/news/uk-57302185	weekend	2021-05-31 16:06:44
ニュース	BBC News - Home	French Open: Norrie through but Konta & Watson out	https://www.bbc.co.uk/sport/tennis/57306900	matches	2021-05-31 16:18:22
ニュース	BBC News - Home	Sinéad O'Connor: My life is actually really boring	https://www.bbc.co.uk/news/entertainment-arts-57305364	connor	2021-05-31 16:38:14
ニュース	BBC News - Home	Copa America to be held in Brazil after Argentina removed as hosts	https://www.bbc.co.uk/sport/football/57304063	argentina	2021-05-31 16:03:19
ニュース	BBC News - Home	Covid-19 in the UK: How many coronavirus cases are there in my area?	https://www.bbc.co.uk/news/uk-51768274	cases	2021-05-31 16:01:03
北海道	北海道新聞	道、１日から追加対策　緊急事態宣言延長	https://www.hokkaido-np.co.jp/article/550232/	緊急事態	2021-06-01 01:17:20