投稿時間:2023-02-03 18:17:27 RSSフィード2023-02-03 18:00 分まとめ(21件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
js	JavaScriptタグが付けられた新着投稿 - Qiita	Node.js で pandas ライクなデータ処理を行う	https://qiita.com/Yasu20220812/items/3d0571d263ebab8f5d41	nodejs	2023-02-03 17:58:17
AWS	AWSタグが付けられた新着投稿 - Qiita	HPKP（証明書ピンニング）について調べた時のあれこれ	https://qiita.com/gon_kojiri/items/c35a43bab0a5f87bf15a	amazon	2023-02-03 17:55:41
AWS	AWSタグが付けられた新着投稿 - Qiita	Route53でCNAMEレコードとAレコードとNSレコードを使って、Wordpressから画像配信	https://qiita.com/uverworldwwww/items/74ff50db8e715d137635	wordpress	2023-02-03 17:46:23
Git	Gitタグが付けられた新着投稿 - Qiita	git fetch解説	https://qiita.com/yunano/items/923e27bd7d3f30350813	gitfetch	2023-02-03 17:26:33
海外TECH	MakeUseOf	elementary OS 7 Is Now Available: Here's What's New	https://www.makeuseof.com/elementary-os-7-is-available-heres-whats-new/	horus	2023-02-03 08:15:15
海外TECH	DEV Community	Support Vector Machines (SVM) Supervised Machine Learning	https://dev.to/anurag629/support-vector-machines-svm-supervised-machine-learning-3lfo	Support Vector Machines SVM Supervised Machine LearningSupport Vector Machines SVM is a widely used Supervised Learning algorithm that is utilized for both Classification and Regression tasks in Machine Learning Although it is primarily employed for Classification problems The objective of the SVM algorithm is to find the best line known as the hyperplane which can effectively divide n dimensional space into different classes This makes it possible to accurately place new data points into the appropriate category In order to determine the hyperplane SVM selects the extreme cases known as support vectors that contribute to its creation The algorithm is named as Support Vector Machine due to these support vectors The following illustration shows two distinct categories being classified by a hyperplane or decision boundary Types of SVMThere are two types of Support Vector Machines SVM Linear SVM This type of SVM is employed for linearly separable data In other words if a dataset can be divided into two classes using a single straight line it is considered linearly separable and a Linear SVM classifier is used for this purpose Non Linear SVM Non Linear SVM is utilized for datasets that cannot be classified using a straight line In such cases this type of SVM is applied to separate the non linearly separated data Working of Linear SVMLinear SVM is a type of SVM that is used for linearly separable data It works by creating the best line or hyperplane that separates the two classes in a two dimensional plane Consider a simple example of a two class classification problem where we have two classes of points in a two dimensional plane as shown below Here we want to separate the blue and red points into two different classes using a line The objective of Linear SVM is to find the line that separates these classes with the maximum margin The margin is the distance between the line and the closest data points from both classes The best line is the one that has the maximum margin which is also known as the maximum margin classifier In this example the line that separates the blue and red points with the maximum margin is the line drawn in green The points closest to the line are called support vectors and they play a crucial role in defining the best line In the above example the support vectors are the points closest to the line as shown by the dotted lines The line that separates the two classes with the maximum margin is the best line and this is what Linear SVM aims to find The hyperplane is then used to classify new data points into either class depending on which side of the line the new data point lies Working of Non Linear SVMNon Linear SVM is a type of SVM used for non linearly separable data In such cases the algorithm transforms the input data into a higher dimensional space where a linear separation becomes possible For example consider a two class classification problem where the data points are not linearly separable in a two dimensional plane as shown below Here it is not possible to separate the blue and red points into two different classes using a straight line To overcome this Non Linear SVM uses a technique called kernel trick where it maps the input data into a higher dimensional space where the data points become linearly separable In the above example the data points are transformed into a three dimensional space using a radial basis function RBF kernel In the higher dimensional space a linear separation is now possible as shown below Here the red and blue points are separated by a hyperplane and this hyperplane is used to classify new data points The mapping from the input data to the higher dimensional space is done implicitly by the Non Linear SVM algorithm and the user does not need to be aware of the mapping In summary Non Linear SVM works by transforming the input data into a higher dimensional space where a linear separation becomes possible and a hyperplane is used to separate the data points into different classes Python implementation of Linear SVMimport numpy as npimport matplotlib pyplot as pltfrom sklearn import datasetsfrom sklearn svm import SVC Load the iris datasetiris datasets load iris X iris data petal length petal widthy iris target Train a linear SVMsvm clf SVC kernel linear C float inf svm clf fit X y Visualize the data and the decision boundaryplt scatter X X c y s cmap plt cm Paired plt show Get the separating hyperplanew svm clf coef b svm clf intercept x min x max X min X max y min y max X min X max xx yy np meshgrid np arange x min x max np arange y min y max Z svm clf predict np c xx ravel yy ravel Z Z reshape xx shape plt contourf xx yy Z cmap plt cm Paired alpha plt xlim xx min xx max plt ylim yy min yy max plt show In the code above we first load the iris dataset and extract the petal length and width as the feature variables We then train a linear SVM using the SVC class from the scikit learn library with kernel linear and C float inf to enforce a hard margin Next we visualize the data and the decision boundary by plotting the data points and the contours of the decision boundary The decision boundary is obtained by calling the predict method on a grid of points in the feature space and reshaping the output to form a D image The final plot shows the decision boundary and the data points with the different colors indicating the different classes in the target variable Python implementation of Non Linear SVMimport numpy as npimport matplotlib pyplot as pltfrom sklearn import datasetsfrom sklearn svm import SVC Load the moons datasetmoons datasets make moons n samples noise X moons y moons Train a non linear SVMsvm clf SVC kernel rbf gamma C svm clf fit X y SVC C gamma Visualize the data and the decision boundaryplt scatter X X c y s cmap plt cm Paired plt show Get the separating hyperplanex min x max X min X max y min y max X min X max xx yy np meshgrid np arange x min x max np arange y min y max Z svm clf predict np c xx ravel yy ravel Z Z reshape xx shape plt contourf xx yy Z cmap plt cm Paired alpha plt xlim xx min xx max plt ylim yy min yy max plt show In the code above we first generate a non linear dataset using the make moons function from the scikit learn library We then train a non linear SVM using the SVC class from the scikit learn library with kernel rbf Radial basis function and gamma and C to control the complexity of the model Next we visualize the data and the decision boundary by plotting the data points and the contours of the decision boundary The decision boundary is obtained by calling the predict method on a grid of points in the feature space and reshaping the output to form a D image The final plot shows the decision boundary and the data points with the different colors indicating the different classes in the target variable GitHub link Complete Data Science BootcampMain Post Complete Data Science Bootcamp	2023-02-03 08:22:05
海外TECH	DEV Community	Create your first WebSockets-based application in NestJS	https://dev.to/leduc1901/create-your-first-websockets-based-application-in-nestjs-2hp	Create your first WebSockets based application in NestJS Introduction What are WebsocketsWebsockets is a communication protocol which provides full duplex communication channels over a single TCP connection established between a web browser and a web server This allows the server to sent to the browser without being called by the client There are many usages of Websockets like chat app or account balance …Where everything needs to be update in real time Websockets in NestJSAccording to NestJS a gateway is simply a class annotated with WebSocketGateway decorator Technically gateways are platform agnostic which makes them compatible with any WebSockets library once an adapter is created Before we dive in writing the application I assume you already know how to create a NestJS application with TypeORM implemented if not you can check out my tutorial right here Set upWe are going to build a simple send and receive message appFirst we need to install some dependenciesyarn add socket io nestjs websockets nestjs platform socket ioThen we will create a Gateway called MessageGatewayexport class MessageGateway implements OnGatewayInit OnGatewayConnection OnGatewayDisconnect Don t forget to add it to our providers in MessageModule Module imports TypeOrmModule forFeature Message controllers MessagesController providers MessagesService MessageGateway export class MessagesModule Build the ServerRight now the IDE will tell you that you need to have some methods called afterInit handleDisconnect handleConnection in MessageGatewayBecause this is just a simple app and we don t actually need to do anything in these methods we will simply log the data out private logger Logger new Logger MessageGateway WebSocketServer wss Server afterInit server Server this logger log Initialized handleDisconnect client Socket this logger log Client Disconnected client id handleConnection client Socket args any this logger log Client Connected client id With this we will know when the Server started which client is connected and disconnected Handle eventsWe need events in this app first is the sendMessage event and the second is receiveMessage event SubscribeMessage sendMessage async handleSendMessage client Socket payload string Promise lt void gt const newMessage await this messagesService createMessage payload this wss emit receiveMessage newMessage With this piece of code we will use SubscribeMessagedecorate to subscribe to the sendMessage event Whenever a sendMessage event occured we will create a new message through messageService Then we emit this created message through receiveMessage event Build the ClientTo test the gateway we need a client I will use the most popular UI library out there to test it ReactWe need to install socket io since the server uses socket ioyarn add socket io clientNext we will create a socket with itimport io from socket io client const socket io http localhost Then we will implement the socket useEffect gt socket on receiveMessage msg gt receiveMessage msg getInitialMessages function getInitialMessages fetch http localhost messages then res gt res json then data gt setMessages data function receiveMessage msg Message const newMessages messages msg setMessages newMessages function sendMessage socket emit sendMessage newMessage setNewMessage You can see that on first render I will subscribe to the receiveMessage event to listen if there is any new event and take action with receiveMessage method Then I get the initial messages through the get apiOn receiveMessage method I append new message to the state Every time we need to send a new message we will emit sendMessage eventThis will be the result ConclusionThis is the most basic example on how to create your first websockets app You will know how to send and receive messages through the gateway If the article is not that clear to you just check out the source code Last WordsAlthough my content is free for everyone but if you find this article helpful you can buy me a coffee here	2023-02-03 08:17:53
医療系	医療介護 CBnews	インフル報告数、全国で「注意報レベル」超え-感染研	https://www.cbnews.jp/news/entry/20230203170057	医療機関	2023-02-03 17:25:00
海外ニュース	Japan Times latest articles	The risks and safety measures to take for backcountry skiers in Japan	https://www.japantimes.co.jp/news/2023/02/03/national/backcountry-skiing-risks-measures/	The risks and safety measures to take for backcountry skiers in JapanThe deaths of two men in Nagano Prefecture including an American champion skier was a tragic reminder of the risks involved in backcountry ski trips	2023-02-03 17:19:40
海外ニュース	Japan Times latest articles	Government considers honoring wheelchair tennis great Shingo Kunieda with People’s Honor Award	https://www.japantimes.co.jp/sports/2023/02/03/tennis/kunieda-honor-award/	Government considers honoring wheelchair tennis great Shingo Kunieda with People s Honor AwardIn January Kunieda a winner of Grand Slam titles and three Paralympic gold medals in the men s singles retired while at the top of	2023-02-03 17:34:10
海外ニュース	Japan Times latest articles	Nick Kyrgios avoids conviction despite pleading guilty to assaulting former girlfriend	https://www.japantimes.co.jp/sports/2023/02/03/tennis/kyrgios-assault-dismissed/	Nick Kyrgios avoids conviction despite pleading guilty to assaulting former girlfriendThe Wimbledon finalist admitted assaulting then girlfriend Chiara Passari on Jan by pushing her to the ground after a heated argument	2023-02-03 17:08:03
海外ニュース	Japan Times latest articles	Lizzo, Foo Fighters and The Strokes top Fuji Rock’s 2023 lineup	https://www.japantimes.co.jp/culture/2023/02/03/music/lizzo-fuji-rock-lineup/	Lizzo Foo Fighters and The Strokes top Fuji Rock s lineupThe premier summer festival will also feature names such as Weezer Yo La Tengo and Denzel Curry underlining the event s return to showcasing non Japanese acts	2023-02-03 17:15:49
ニュース	BBC News - Home	Chinese spy balloon: US tracks suspected surveillance device	https://www.bbc.co.uk/news/world-us-canada-64507225?at_medium=RSS&at_campaign=KARANGA	deviceofficials	2023-02-03 08:02:19
ニュース	BBC News - Home	Infected blood scandal: Five things we have learned	https://www.bbc.co.uk/news/health-64497868?at_medium=RSS&at_campaign=KARANGA	evidence	2023-02-03 08:40:39
サブカルネタ	ラーブロ	GOOD MORNING ラーメンショップ@穴守稲荷2023今年1発目の投稿は、や...	http://ra-blog.net/modules/rssc/single_feed.php?fid=207514	goodmorning	2023-02-03 08:13:55
ニュース	Newsweek	復帰した「世界一のモデル」 ノーブラ、Tバック、シースルードレスでビーチに降臨	https://www.newsweekjapan.jp/stories/culture/2023/02/-t.php		2023-02-03 17:50:00
マーケティング	MarkeZine	三井住友カードに学ぶ、最新エリアマーケティング　消費傾向に基づく商圏分析とは？【視聴無料】	http://markezine.jp/article/detail/41221	三井住友カード	2023-02-03 17:30:00
IT	週刊アスキー	チケットを取る際に座席からの球場の眺めを確認できる次世代型パノラマビューシステム「Seating Experience Assistant Technology」	https://weekly.ascii.jp/elem/000/004/123/4123428/	rienceassistanttechnology	2023-02-03 17:30:00
IT	週刊アスキー	グリーンハウス、「Google TV搭載4K／HDR対応50V型液晶テレビ（GH-GTV50AG-BK）」をゲオ限定で発売	https://weekly.ascii.jp/elem/000/004/123/4123423/	ghgtvagbk	2023-02-03 17:15:00
IT	週刊アスキー	ハイペリオンガンダムが実装！『GUNDAM EVOLUTION』で3ヵ月連続ユニット追加「HYPER UPDATE」開始	https://weekly.ascii.jp/elem/000/004/123/4123425/	gundamevolution	2023-02-03 17:05:00
マーケティング	AdverTimes	新田ゼラチン「フレイルFREE Project」PR発表会開催 優木まおみがヨガ実演も	https://www.advertimes.com/20230203/article410648/	新田ゼラチン「フレイルFREEProject」PR発表会開催優木まおみがヨガ実演もコラーゲンを基軸に素材のp製造・販売をてがける新田ゼラチンは月日、一般消費者向けに「フレイル予防」を促進する「フレイルFREEProject」のPR発表会を開催した。	2023-02-03 08:30:08