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Curso completo de Desenvolvimento Web Crie 6 projetosHTML JAVASCRIPT CSS PHP 8. Bootstrap - Criando Sites Responsivos 14. Botões - (Arquivos da aula)

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This file was stored at "C:\Users\user\Documents\file-to-base64.html" Your web browsers needs to have JavaScript enabled in order for this to work. Complete source code of HTML file "file-to-base64.html": < !DOCTYPE html > < html lang="en" > < head > < meta charset="utf-8" / > < title > File to Base64 with JavaScript in 400 characters < /title > < /head > < input type="file" / > < p id="out" > < /p > < script > var input = document.querySelector('input[type=file]'); input.onchange = function () {   var file = input.files[0],   reader = new FileReader();     reader.onloadend = function ()     {       var b64 = reader.result       .replace(/^data:.+;base64,/, ''); /*comment out this line for Base64 as a data URI*/       document.getElementById("out").innerHTML = b64;     };   reader.readAsDataURL(file); }; < /script > < /html > Sources: * Add where I got the code from, which I later modified [done - no] * 400 characters inside the script tag, according to https://thewordcounter.com/character-counter/ * Valid HTML according to https://validator.w3.org/nu/#file

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Big Microsoft's Xbox One Update

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JavaScript Jabber celebrates its 400th episode with former host Dave Smith and some other familiar voices. Each of the panelists talks about what they’ve been up to. Dave hasn’t been on the show for 3 years, but he and Jameson Dance have started a podcast called Soft Skills Engineering where they answer questions about the non-technical side of engineering. When he left the show he was the director of engineering on Hire View, and currently he works for Amazon on Alexa.  Christopher Buecheler has been on several JSJ, RRU, and MJS episodes. His time is divided between contracting for startups and his own company closebrace.com, a tutorial and resource site for JavaScript developers.  Dan Shapir has also been on JSJ as a guest, and is currently works for Wix doing performance tech. He enjoys speaking at conferences, such as JS Camp in Bucharest, Romania and the YGLF conference. Steve Edwards was previously on MJS 078. He started on Drupal in the PHP world, switched to JavaScript, and then a few years ago he started looking at Vue. Now he does Vue fulltime for ImageWare Systems. As for Charles, his primary focus is the podcasts, since DevChat.tv produces around 20 episodes per week. 5 new shows were started in July, and he talks about some of the challenges that that brought. One of his most popular shows recently was JSJ 389: What makes a 10x Engineer? This helped him realize that he wants to help teach people how to be a successful engineer, so he’s working on launching a new show about it.  The panelists share some of their favorite JSJ episodes. They discuss the tendency of JSJ to get early access to these fascinating people when the conversation was just beginning, such as the inventor of Redux Dan Abramov, before their rise to stardom. The talk about the rise in popularity of podcasting in general. They agree that even though JavaScript is evolving and changing quickly, it’s still helpful to listen to old episodes.  Charles talks about the influence JavaScript Jabber has had on other podcasts. It has spawned several spinoffs, including My JavaScript Story. He’s had several hosts start their own DevChat.tv shows based off JavaScript Jabber, including Adventures in Angular and The DevEd Podcast. JavaScript Jabber has also been the inspiration for other podcasts that aren’t part of DevChat.tv. There aren’t many podcast companies that produce as many shows as they do and they’re developing their own tools. DevChat.tv moved off of WordPress and is in the process of moving over to Podwrench. Charles talks about all the new shows that have been launched, and his view on ‘competing’ podcasts. Charles is also considering doing an audio drama that happens in a programming office, so if you would like to write and/or voice that  show, he invites you to contact him.  The show concludes with the panel talking about the projects they’ve been working on that they want listeners to check out. Christopher invites listeners to check out closebrace.com. He also has plans to write a short ebook on unit testing with jest, considered doing his own podcast, and invites people to check out his fiction books on his website. Dan talks about his involvement with Wix, a drag and drop website service, that recently released a technology called Corvid which lets you write JS into the website you build with Wix. This means you can design your user interface using Wix, but then automate it, add events functionality, etc. Dan is also going to be at the Chrome Dev Summit conference. Dave invites listeners to check out the Soft Skills Engineering podcast, and Charles invites listeners to subscribe to his new site maxcoders.io.  Panelists ? ? Dan Shapir ? ? ? Christopher Buecheler ? ? ? Steve Edwards ? ? ? Dave Smith ? ? ? Charles Max Wood ? Sponsors ? ? Tidelift ? ? ? Sentry use the code “devchat” for 2 months free on Sentry’s small plan ? ? ? Adventures in .NET ? Links ? ? The Dev Rev ? ? ? MJS 099: Christopher Buecheler ? ? ? JSJ 338: It's Supposed to Hurt. Get Outside of Your Comfort Zone to Master Your Craft with Christopher Buecheler ? ? ? RRU 029: Christopher Buecheler Getting Ready to Teach Lessons Learned from Building an 84 Tutorial Software Course ? ? ? MJS 108: Dan Shapir ? ? ? JSJ 334: Web Performance API with Dan Shapir ? ? ? JSJ 371: The Benefits and Challenges of Server Side Rendering with Dan Shapir ? ? ? MJS 078: Steve Edwards ? ? ? JSJ 179: Redux and React with Dan Abramov ? ? ? JSJ 187: Vue.js with Evan You ? ? ? JSJ 383: What is JavaScript? ? ? ? JSJ 385: What Can You Build with JavaScript ? ? ? JSJ 390: Transposit with Adam Leventhal ? ? ? JSJ 395: The New Ember with Mike North ? ? ? JSJ 220: Teaching JavaScript with Kyle Simpson ? ? ? JSJ 313: Light Functional JavaScript with Kyle Simpson ? ? ? JSJ 124: The Origin of JavaScript with Brendan Eich ? ? ? JSJ 073: React with Pete Hunt and Jordan Walke ? ? ? JSJ 392: The Murky Past and Misty Future of JavaScript with Douglas Crockford ? ? ? JSJ 391: Debugging with Todd Gardner ? ? ? JSJ 389: What Makes a 10x Engineer? ? ? ? cwbuecheler.com  ? ? ? Closebrace.com ? ? ? Corvid by Wix   ? ? ? Soft Skills Engineering podcast ? ? ? maxcoders.io                                                                                                                                                                            ? Follow DevChatTV on Facebook and Twitter Picks Steve Edwards: ? ? form.io ? Christopher Buecheler: ? ? Apollo GraphQL Playground ? ? ? @TheTimeCowboy Jake Lawrence ? Charles Max Wood: ? ? St. George Marathon ? ? ? GU Energy Original Sports Nutrition Energy Gel ? ? ? Vrbo ? ? ? devchat.tv/15minutes ? Dan Shapir: ? ? Revolutions by Mike Duncan podcast ? ? ? The Winter of the World book series ? Dave Smith: ? ? 13 Minutes to the Moon podcast by BBC ? ? ? The Mind ?

On today's episode, Charles, Joe, and Cory discuss  Practical JavaScript with Gordon Zhu . Gordon is the founder of Watch and Code, and teaches the Practical JavaScript online course. His mission is to help beginners become developers through tutorials. Tune in!

The panelists discuss The New York Times and JavaScript with Eitan Konigsburg, Alastair Coote and Reed Emmons.

In today's all-panelist episode, we take a deep dive into some of the nooks and crannies of JavaScript. We discuss and debate the benefits and problems of various methods such as getter and setter, Const, Freeze and Proxy. SponsorsTop End DevsRaygun | Click here to get started on your free 14-day trialCoaching | Top End DevsPicksAJ - Valley Forge FlagsAJ - Frog ChessAJ - Beyond Code BootcampAJ - Creeds of CraftsmanshipDan - JSConf BudapestDan - Stock Market / Tech Stocks going downDan - War in UkraineSteve - Dad JokesSponsored By:Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your DreamsRaygun: Raygun now offers Real User Monitoring of Core Web Vitals. Start your 14-day free trial now.Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Steve and AJ talk with Max Kordek, founder of his startup Lisk, which is geared towards helping JavaScript developers use the blockchain to develop new applications for new industries. We delve deep into the origins and base technologies of the blockchain, how it has been used, and how it can be used in the future. They also discuss Lisk, it's purpose, and how Max hopes their SDK will be used by developers to explore the blockchain and find brand new applications for it.PanelAJ O'NealSteve EdwardsGuestMax KordekSponsorsDev Influencers AcceleratorRaygun | Click here to get started on your free 14-day trialPodcastBootcamp.ioLinksGitHub | LiskHackonLiskLiskMastering blockchain: Meet Lisk, a blockchain platform for JavaScript developersBlog Archives | LiskIntroducing the Lisk Grant ProgramThe 5th Anniversary of Lisk NetworkIntroducing Lisk Interoperability - YouTubeLisk International - YouTubeEvents - YouTubeLisk - DiscordLisk CommunityLisk - RedditLisk - YouTubeTwitter: Lisk ( @LiskHQ )Max Kordek - YouTubeTwitter: Max Kordek | HODLing the Lisk Gem ( @maxkordek )PicksAJ- Blockchain Backer on TeachableAJ- Blockchain Backer - YouTubeAJ- HashcashAJ- Cryptocurrency is an abject disasterAJ- Nyan Cat NFTAJ- Walmart urges its suppliers to use IBM blockchain technologyAJ- Ep. 139 - Smart Contracts & Oracles - insights from ChainlinkMax- Technology | NASAContact AJ:AJ ONealCoolAJ86 on GITBeyond Code BootcampBeyond Code Bootcamp | GitHubFollow Beyond Code Bootcamp | FacebookTwitter: Beyond Code Bootcamp ( @_beyondcode )Contact Steve:Twitter: Steve Edwards ( @wonder95 )GitHub: Steve Edwards ( wonder95 )LinkedIn: Steve EdwardsSpecial Guest: Max Kordek.Sponsored By:Podcast Bootcamp: Launch an Amazing Sounding Podcast in just 4 WEEKS! Work with a 13 year podcasting veteran to get your podcast started off on the right foot! Raygun: Raygun now offers Real User Monitoring of Core Web Vitals. Start your 14-day free trial now.

Curso completo de Desenvolvimento Web Crie 6 projetosHTML JAVASCRIPT CSS PHP 13. Funções PHP 7. Funções básicas - Validação de formulários

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Curso completo de Desenvolvimento Web Crie 6 projetosHTML JAVASCRIPT CSS PHP 14. Orientação a objetos PHP 7. Calculadora (Projeto final)

Perma.cc archive of https://stackoverflow.com/questions/32553158/detect-click-outside-react-component created on 2021-11-29 08:06:14+00:00.

CompletePathToJavaScriptMastery - Complete Path to JavaScript Mastery 2020-11

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Curso completo de Desenvolvimento Web Crie 6 projetosHTML JAVASCRIPT CSS PHP 3. HTML 15. Formulários

It´s a simple Javascript sign

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PukiWiki: JSDB(JavaScript for DataBase) Dumped with PukiWiki-Dumper v0.0.14, and uploaded with pukiWikiUploader v0.0.14.

A look at the built in Regex capabilities of Javascript

Learning Php Mysql Javascript Css Html 5 Robin Nixon 3e Urduinpage.com

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📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings JavaScript Algorithms and Data Structures This repository contains JavaScript based examples of manypopular algorithms and data structures. Each algorithm and data structure has its own separate READMEwith related explanations and links for further reading (including onesto YouTube videos). Read this in other languages: 简体中文 , 繁體中文 , 한국어 , 日本語 , Polski , Français , Español , Português , Русский , Türk , Italiana ☝ Note that this project is meant to be used for learning and researching purposes only, and it is not meant to be used for production. Data Structures A data structure is a particular way of organizing and storing data in a computer so that it canbe accessed and modified efficiently. More precisely, a data structure is a collection of datavalues, the relationships among them, and the functions or operations that can be applied tothe data. B - Beginner, A - Advanced B Linked List B Doubly Linked List B Queue B Stack B Hash Table B Heap - max and min heap versions B Priority Queue A Trie A Tree A Binary Search Tree A AVL Tree A Red-Black Tree A Segment Tree - with min/max/sum range queries examples A Fenwick Tree (Binary Indexed Tree) A Graph (both directed and undirected) A Disjoint Set A Bloom Filter Algorithms An algorithm is an unambiguous specification of how to solve a class of problems. It isa set of rules that precisely define a sequence of operations. B - Beginner, A - Advanced Algorithms by Topic Math B Bit Manipulation - set/get/update/clear bits, multiplication/division by two, make negative etc. B Factorial B Fibonacci Number - classic and closed-form versions B Primality Test (trial division method) B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Least Common Multiple (LCM) B Sieve of Eratosthenes - finding all prime numbers up to any given limit B Is Power of Two - check if the number is power of two (naive and bitwise algorithms) B Pascal's Triangle B Complex Number - complex numbers and basic operations with them B Radian & Degree - radians to degree and backwards conversion B Fast Powering B Horner's method - polynomial evaluation A Integer Partition A Square Root - Newton's method A Liu Hui π Algorithm - approximate π calculations based on N-gons A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Sets B Cartesian Product - product of multiple sets B Fisher–Yates Shuffle - random permutation of a finite sequence A Power Set - all subsets of a set (bitwise and backtracking solutions) A Permutations (with and without repetitions) A Combinations (with and without repetitions) A Longest Common Subsequence (LCS) A Longest Increasing Subsequence A Shortest Common Supersequence (SCS) A Knapsack Problem - "0/1" and "Unbound" ones A Maximum Subarray - "Brute Force" and "Dynamic Programming" (Kadane's) versions A Combination Sum - find all combinations that form specific sum Strings B Hamming Distance - number of positions at which the symbols are different A Levenshtein Distance - minimum edit distance between two sequences A Knuth–Morris–Pratt Algorithm (KMP Algorithm) - substring search (pattern matching) A Z Algorithm - substring search (pattern matching) A Rabin Karp Algorithm - substring search A Longest Common Substring A Regular Expression Matching Searches B Linear Search B Jump Search (or Block Search) - search in sorted array B Binary Search - search in sorted array B Interpolation Search - search in uniformly distributed sorted array Sorting B Bubble Sort B Selection Sort B Insertion Sort B Heap Sort B Merge Sort B Quicksort - in-place and non-in-place implementations B Shellsort B Counting Sort B Radix Sort Linked Lists B Straight Traversal B Reverse Traversal Trees B Depth-First Search (DFS) B Breadth-First Search (BFS) Graphs B Depth-First Search (DFS) B Breadth-First Search (BFS) B Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Dijkstra Algorithm - finding shortest paths to all graph vertices from single vertex A Bellman-Ford Algorithm - finding shortest paths to all graph vertices from single vertex A Floyd-Warshall Algorithm - find shortest paths between all pairs of vertices A Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions) A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Topological Sorting - DFS method A Articulation Points - Tarjan's algorithm (DFS based) A Bridges - DFS based algorithm A Eulerian Path and Eulerian Circuit - Fleury's algorithm - Visit every edge exactly once A Hamiltonian Cycle - Visit every vertex exactly once A Strongly Connected Components - Kosaraju's algorithm A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city Cryptography B Polynomial Hash - rolling hash function based on polynomial B Caesar Cipher - simple substitution cipher Machine Learning B NanoNeuron - 7 simple JS functions that illustrate how machines can actually learn (forward/backward propagation) Uncategorized B Tower of Hanoi B Square Matrix Rotation - in-place algorithm B Jump Game - backtracking, dynamic programming (top-down + bottom-up) and greedy examples B Unique Paths - backtracking, dynamic programming and Pascal's Triangle based examples B Rain Terraces - trapping rain water problem (dynamic programming and brute force versions) B Recursive Staircase - count the number of ways to reach to the top (4 solutions) A N-Queens Problem A Knight's Tour Algorithms by Paradigm An algorithmic paradigm is a generic method or approach which underlies the design of a classof algorithms. It is an abstraction higher than the notion of an algorithm, just as analgorithm is an abstraction higher than a computer program. Brute Force - look at all the possibilities and selects the best solution B Linear Search B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top A Maximum Subarray A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Greedy - choose the best option at the current time, without any consideration for the future B Jump Game A Unbound Knapsack Problem A Dijkstra Algorithm - finding shortest path to all graph vertices A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph Divide and Conquer - divide the problem into smaller parts and then solve those parts B Binary Search B Tower of Hanoi B Pascal's Triangle B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Merge Sort B Quicksort B Tree Depth-First Search (DFS) B Graph Depth-First Search (DFS) B Jump Game B Fast Powering A Permutations (with and without repetitions) A Combinations (with and without repetitions) Dynamic Programming - build up a solution using previously found sub-solutions B Fibonacci Number B Jump Game B Unique Paths B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top A Levenshtein Distance - minimum edit distance between two sequences A Longest Common Subsequence (LCS) A Longest Common Substring A Longest Increasing Subsequence A Shortest Common Supersequence A 0/1 Knapsack Problem A Integer Partition A Maximum Subarray A Bellman-Ford Algorithm - finding shortest path to all graph vertices A Floyd-Warshall Algorithm - find shortest paths between all pairs of vertices A Regular Expression Matching Backtracking - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you testif it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on adifferent path of finding a solution. Normally the DFS traversal of state-space is being used. B Jump Game B Unique Paths B Power Set - all subsets of a set A Hamiltonian Cycle - Visit every vertex exactly once A N-Queens Problem A Knight's Tour A Combination Sum - find all combinations that form specific sum Branch & Bound - remember the lowest-cost solution found at each stage of the backtrackingsearch, and use the cost of the lowest-cost solution found so far as a lower bound on the cost ofa least-cost solution to the problem, in order to discard partial solutions with costs larger than thelowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-spacetree is being used. How to use this repository Install all dependencies npm install Run ESLint You may want to run it to check code quality. npm run lint Run all tests npm test Run tests by name npm test -- 'LinkedList' Playground You may play with data-structures and algorithms in ./src/playground/playground.js file and writetests for it in ./src/playground/__test__/playground.test.js . Then just simply run the following command to test if your playground code works as expected: npm test -- 'playground' Useful Information References ▶ Data Structures and Algorithms on YouTube Big O Notation Big O notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows.On the chart below you may find most common orders of growth of algorithms specified in Big O notation. Source: Big O Cheat Sheet . Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data. | Big O Notation | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements || -------------- | ---------------------------- | ----------------------------- | ------------------------------- || O(1) | 1 | 1 | 1 || O(log N) | 3 | 6 | 9 || O(N) | 10 | 100 | 1000 || O(N log N) | 30 | 600 | 9000 || O(N^2) | 100 | 10000 | 1000000 || O(2^N) | 1024 | 1.26e+29 | 1.07e+301 || O(N!) | 3628800 | 9.3e+157 | 4.02e+2567 | Data Structure Operations Complexity | Data Structure | Access | Search | Insertion | Deletion | Comments || ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- || Array | 1 | n | n | n | || Stack | n | n | 1 | 1 | || Queue | n | n | 1 | 1 | || Linked List | n | n | 1 | n | || Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) || Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) || B-Tree | log(n) | log(n) | log(n) | log(n) | || Red-Black Tree | log(n) | log(n) | log(n) | log(n) | || AVL Tree | log(n) | log(n) | log(n) | log(n) | || Bloom Filter | - | 1 | 1 | - | False positives are possible while searching | Array Sorting Algorithms Complexity | Name | Best | Average | Worst | Memory | Stable | Comments || --------------------- | :-------------: | :-----------------: | :-----------------: | :-------: | :-------: | :-------- || Bubble sort | n | n 2 | n 2 | 1 | Yes | || Insertion sort | n | n 2 | n 2 | 1 | Yes | || Selection sort | n 2 | n 2 | n 2 | 1 | No | || Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | || Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | || Quick sort | n log(n) | n log(n) | n 2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space || Shell sort | n log(n) | depends on gap sequence | n (log(n)) 2 | 1 | No | || Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array || Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key | Project Backers You may support this project via ❤️️ GitHub or ❤️️ Patreon . Folks who are backing this project ∑ = 1 To restore the repository download the bundle wget https://archive.org/download/github.com-trekhleb-javascript-algorithms_-_2020-12-10_01-34-51/trekhleb-javascript-algorithms_-_2020-12-10_01-34-51.bundle and run: git clone trekhleb-javascript-algorithms_-_2020-12-10_01-34-51.bundle Source: https://github.com/trekhleb/javascript-algorithms Uploader: trekhleb Upload date: 2020-12-10

📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings JavaScript Algorithms and Data Structures 🇺🇦 UKRAINE IS BEING ATTACKED BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED. - Help Ukraine via National Bank of Ukraine - Help Ukraine via SaveLife fund - More info on war.ukraine.ua and MFA of Ukraine This repository contains JavaScript based examples of manypopular algorithms and data structures. Each algorithm and data structure has its own separate READMEwith related explanations and links for further reading (including onesto YouTube videos). Read this in other languages: 简体中文 , 繁體中文 , 한국어 , 日本語 , Polski , Français , Español , Português , Русский , Türkçe , Italiana , Bahasa Indonesia , Українська , Arabic , Tiếng Việt , Deutsch ☝ Note that this project is meant to be used for learning and researching purposesonly, and it is not meant to be used for production. Data Structures A data structure is a particular way of organizing and storing data in a computer so that it canbe accessed and modified efficiently. More precisely, a data structure is a collection of datavalues, the relationships among them, and the functions or operations that can be applied tothe data. B - Beginner, A - Advanced B Linked List B Doubly Linked List B Queue B Stack B Hash Table B Heap - max and min heap versions B Priority Queue A Trie A Tree A Binary Search Tree A AVL Tree A Red-Black Tree A Segment Tree - with min/max/sum range queries examples A Fenwick Tree (Binary Indexed Tree) A Graph (both directed and undirected) A Disjoint Set A Bloom Filter Algorithms An algorithm is an unambiguous specification of how to solve a class of problems. It isa set of rules that precisely define a sequence of operations. B - Beginner, A - Advanced Algorithms by Topic Math B Bit Manipulation - set/get/update/clear bits, multiplication/division by two, make negative etc. B Binary Floating Point - binary representation of the floating-point numbers. B Factorial B Fibonacci Number - classic and closed-form versions B Prime Factors - finding prime factors and counting them using Hardy-Ramanujan's theorem B Primality Test (trial division method) B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Least Common Multiple (LCM) B Sieve of Eratosthenes - finding all prime numbers up to any given limit B Is Power of Two - check if the number is power of two (naive and bitwise algorithms) B Pascal's Triangle B Complex Number - complex numbers and basic operations with them B Radian & Degree - radians to degree and backwards conversion B Fast Powering B Horner's method - polynomial evaluation B Matrices - matrices and basic matrix operations (multiplication, transposition, etc.) B Euclidean Distance - distance between two points/vectors/matrices A Integer Partition A Square Root - Newton's method A Liu Hui π Algorithm - approximate π calculations based on N-gons A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Sets B Cartesian Product - product of multiple sets B Fisher–Yates Shuffle - random permutation of a finite sequence A Power Set - all subsets of a set (bitwise and backtracking solutions) A Permutations (with and without repetitions) A Combinations (with and without repetitions) A Longest Common Subsequence (LCS) A Longest Increasing Subsequence A Shortest Common Supersequence (SCS) A Knapsack Problem - "0/1" and "Unbound" ones A Maximum Subarray - "Brute Force" and "Dynamic Programming" (Kadane's) versions A Combination Sum - find all combinations that form specific sum Strings B Hamming Distance - number of positions at which the symbols are different B Palindrome - check if the string is the same in reverse A Levenshtein Distance - minimum edit distance between two sequences A Knuth–Morris–Pratt Algorithm (KMP Algorithm) - substring search (pattern matching) A Z Algorithm - substring search (pattern matching) A Rabin Karp Algorithm - substring search A Longest Common Substring A Regular Expression Matching Searches B Linear Search B Jump Search (or Block Search) - search in sorted array B Binary Search - search in sorted array B Interpolation Search - search in uniformly distributed sorted array Sorting B Bubble Sort B Selection Sort B Insertion Sort B Heap Sort B Merge Sort B Quicksort - in-place and non-in-place implementations B Shellsort B Counting Sort B Radix Sort Linked Lists B Straight Traversal B Reverse Traversal Trees B Depth-First Search (DFS) B Breadth-First Search (BFS) Graphs B Depth-First Search (DFS) B Breadth-First Search (BFS) B Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Dijkstra Algorithm - finding the shortest paths to all graph vertices from single vertex A Bellman-Ford Algorithm - finding the shortest paths to all graph vertices from single vertex A Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices A Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions) A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Topological Sorting - DFS method A Articulation Points - Tarjan's algorithm (DFS based) A Bridges - DFS based algorithm A Eulerian Path and Eulerian Circuit - Fleury's algorithm - Visit every edge exactly once A Hamiltonian Cycle - Visit every vertex exactly once A Strongly Connected Components - Kosaraju's algorithm A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city Cryptography B Polynomial Hash - rolling hash function based on polynomial B Rail Fence Cipher - a transposition cipher algorithm for encoding messages B Caesar Cipher - simple substitution cipher B Hill Cipher - substitution cipher based on linear algebra Machine Learning B NanoNeuron - 7 simple JS functions that illustrate how machines can actually learn (forward/backward propagation) B k-NN - k-nearest neighbors classification algorithm B k-Means - k-Means clustering algorithm Image Processing B Seam Carving - content-aware image resizing algorithm Statistics B Weighted Random - select the random item from the list based on items' weights Evolutionary algorithms A Genetic algorithm - example of how the genetic algorithm may be applied for training the self-parking cars Uncategorized B Tower of Hanoi B Square Matrix Rotation - in-place algorithm B Jump Game - backtracking, dynamic programming (top-down + bottom-up) and greedy examples B Unique Paths - backtracking, dynamic programming and Pascal's Triangle based examples B Rain Terraces - trapping rain water problem (dynamic programming and brute force versions) B Recursive Staircase - count the number of ways to reach to the top (4 solutions) B Best Time To Buy Sell Stocks - divide and conquer and one-pass examples A N-Queens Problem A Knight's Tour Algorithms by Paradigm An algorithmic paradigm is a generic method or approach which underlies the design of a classof algorithms. It is an abstraction higher than the notion of an algorithm, just as analgorithm is an abstraction higher than a computer program. Brute Force - look at all the possibilities and selects the best solution B Linear Search B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top A Maximum Subarray A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Greedy - choose the best option at the current time, without any consideration for the future B Jump Game A Unbound Knapsack Problem A Dijkstra Algorithm - finding the shortest path to all graph vertices A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph Divide and Conquer - divide the problem into smaller parts and then solve those parts B Binary Search B Tower of Hanoi B Pascal's Triangle B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Merge Sort B Quicksort B Tree Depth-First Search (DFS) B Graph Depth-First Search (DFS) B Matrices - generating and traversing the matrices of different shapes B Jump Game B Fast Powering B Best Time To Buy Sell Stocks - divide and conquer and one-pass examples A Permutations (with and without repetitions) A Combinations (with and without repetitions) A Maximum Subarray Dynamic Programming - build up a solution using previously found sub-solutions B Fibonacci Number B Jump Game B Unique Paths B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top B Seam Carving - content-aware image resizing algorithm A Levenshtein Distance - minimum edit distance between two sequences A Longest Common Subsequence (LCS) A Longest Common Substring A Longest Increasing Subsequence A Shortest Common Supersequence A 0/1 Knapsack Problem A Integer Partition A Maximum Subarray A Bellman-Ford Algorithm - finding the shortest path to all graph vertices A Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices A Regular Expression Matching Backtracking - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you testif it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on adifferent path of finding a solution. Normally the DFS traversal of state-space is being used. B Jump Game B Unique Paths B Power Set - all subsets of a set A Hamiltonian Cycle - Visit every vertex exactly once A N-Queens Problem A Knight's Tour A Combination Sum - find all combinations that form specific sum Branch & Bound - remember the lowest-cost solution found at each stage of the backtrackingsearch, and use the cost of the lowest-cost solution found so far as a lower bound on the cost ofa least-cost solution to the problem, in order to discard partial solutions with costs larger than thelowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-spacetree is being used. How to use this repository Install all dependencies npm install Run ESLint You may want to run it to check code quality. npm run lint Run all tests npm test Run tests by name npm test -- 'LinkedList' Troubleshooting If linting or testing is failing, try to delete the node_modules folder and re-install npm packages: rm -rf ./node_modulesnpm i Also make sure that you're using a correct Node version ( &gt;=14.16.0 ). If you're using nvm for Node version management you may run nvm use from the root folder of the project and the correct version will be picked up. Playground You may play with data-structures and algorithms in ./src/playground/playground.js file and writetests for it in ./src/playground/__test__/playground.test.js . Then just simply run the following command to test if your playground code works as expected: npm test -- 'playground' Useful Information References ▶ Data Structures and Algorithms on YouTube ✍🏻 Data Structure Sketches Big O Notation Big O notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows.On the chart below you may find most common orders of growth of algorithms specified in Big O notation. Source: Big O Cheat Sheet . Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data. | Big O Notation | Type | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements || -------------- | ----------- | ---------------------------- | ----------------------------- | ------------------------------- || O(1) | Constant | 1 | 1 | 1 || O(log N) | Logarithmic | 3 | 6 | 9 || O(N) | Linear | 10 | 100 | 1000 || O(N log N) | n log(n) | 30 | 600 | 9000 || O(N^2) | Quadratic | 100 | 10000 | 1000000 || O(2^N) | Exponential | 1024 | 1.26e+29 | 1.07e+301 || O(N!) | Factorial | 3628800 | 9.3e+157 | 4.02e+2567 | Data Structure Operations Complexity | Data Structure | Access | Search | Insertion | Deletion | Comments || ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- || Array | 1 | n | n | n | || Stack | n | n | 1 | 1 | || Queue | n | n | 1 | 1 | || Linked List | n | n | 1 | n | || Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) || Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) || B-Tree | log(n) | log(n) | log(n) | log(n) | || Red-Black Tree | log(n) | log(n) | log(n) | log(n) | || AVL Tree | log(n) | log(n) | log(n) | log(n) | || Bloom Filter | - | 1 | 1 | - | False positives are possible while searching | Array Sorting Algorithms Complexity | Name | Best | Average | Worst | Memory | Stable | Comments || --------------------- | :-------------: | :-----------------: | :-----------------: | :-------: | :-------: | :-------- || Bubble sort | n | n 2 | n 2 | 1 | Yes | || Insertion sort | n | n 2 | n 2 | 1 | Yes | || Selection sort | n 2 | n 2 | n 2 | 1 | No | || Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | || Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | || Quick sort | n log(n) | n log(n) | n 2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space || Shell sort | n log(n) | depends on gap sequence | n (log(n)) 2 | 1 | No | || Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array || Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key | Project Backers You may support this project via ❤️️ GitHub or ❤️️ Patreon . Folks who are backing this project ∑ = 0 ℹ️ A few more projects and articles about JavaScript and algorithms on trekhleb.dev Author @trekhleb To restore the repository download the bundle wget https://archive.org/download/github.com-trekhleb-javascript-algorithms_-_2022-12-07_23-11-38/trekhleb-javascript-algorithms_-_2022-12-07_23-11-38.bundle and run: git clone trekhleb-javascript-algorithms_-_2022-12-07_23-11-38.bundle Source: https://github.com/trekhleb/javascript-algorithms Uploader: trekhleb Upload date: 2022-12-07

JavaScript 教程 本教程全面介绍 JavaScript 核心语法，从最简单的开始讲起，循序渐进、由浅入深，力求清晰易懂。所有章节都带有大量的代码实例，便于理解和模仿，可以用到实际项目中，即学即用。 本教程适合初学者当作 JavaScript 语言的入门教程，也适合当作日常使用的参考手册。 To restore the repository download the bundle wget https://archive.org/download/github.com-wangdoc-javascript-tutorial_-_2017-12-25_05-13-04/wangdoc-javascript-tutorial_-_2017-12-25_05-13-04.bundle and run: git clone wangdoc-javascript-tutorial_-_2017-12-25_05-13-04.bundle Source: https://github.com/wangdoc/javascript-tutorial Uploader: wangdoc Upload date: 2017-12-25