Алгоритмы структуры данных javascript

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings

License

trekhleb/javascript-algorithms

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

JavaScript Algorithms and Data Structures

This repository contains JavaScript based examples of many popular algorithms and data structures.

Each algorithm and data structure has its own separate README with related explanations and links for further reading (including ones to YouTube videos).

☝ 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.

A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

• 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)

  An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.

  • 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
    • 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, backtracking, and cascading 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
    • 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
    • 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
    • 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
    • B Bucket Sort
    • B Straight Traversal
    • B Reverse Traversal
    • B Depth-First Search (DFS)
    • B Breadth-First Search (BFS)
    • 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
    • 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
    • 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
    • B Seam Carving — content-aware image resizing algorithm
    • B Weighted Random — select the random item from the list based on items’ weights
    • A Genetic algorithm — example of how the genetic algorithm may be applied for training the self-parking cars
    • 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

    An algorithmic paradigm is a generic method or approach which underlies the design of a class of algorithms. It is an abstraction higher than the notion of an algorithm, just as an algorithm 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
      • 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
      • 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
      • 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
      • 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

      How to use this repository

      Install all dependencies

      You may want to run it to check code quality.

      Источник

      Классические алгоритмы и структуры данных на JavaScript

      Привет Всем! Я недавно запустил на GitHub проект JavaScript Algorithms and Data Structures, который содержит примеры классических алгоритмов и структур данных написанных на JavaScript с объяснениями, примерами и ссылками для дальнейшего изучения (в частности на соответствующие YouTube видео).

      Основная задача проекта — помочь программистам в изучении и применении алгоритмов и сделать это на JavaScript-е.

      Для того, чтобы сделать процесс изучения более понятным я постарался добавить графические иллюстрации для каждого алгоритма и структуры данных, чтобы быстрее визуально понять о чем идет речь и как тот или иной алгоритм функционирует.

      Так же в корневом README вы найдете справочную информацию, которая может пригодиться при изучении. Например:

      • Графики Big O нотации (чтобы визуально быстро уловить разницу между O(n!) и O(n^2) )
      • Список с конкретными значениями Big O (чтобы понимать насколько большое или маленькое значение у 10! (а оно у него аж 3628800))
      • Сложность выполнения базовых операций для структур данных (чтобы понять у каких структур быстрое чтение, а у каких поиск или удаление)
      • Сравнительная таблица сложности алгоритмов сортировки (чтобы понять какой способ сортировки выбрать и в каком случае, стабильная ли сортировка или нет)

      Репозиторий так же содержит песочницу. Это небольшой шаблон функции и пустого теста для нее, который должен помочь программисту сразу же приступить к экспериментам с алгоритмами, вместо того, чтобы создавать заново шаблонный код.

      На данный момент в репозитории реализованы следующие структуры данных:

      • Linked List
      • Queue
      • Stack
      • Hash Table
      • Heap
      • Priority Queue
      • Trie
      • Tree (Binary Search Tree, AVL Tree)
      • Graph (both directed and undirected)
      • Disjoint Set
      • Brute Force Algorithms (перебор всех возможных комбинаций и выбор правильного решения)
      • Greedy Algorithms (выбор оптимального решения на каждом текущем шаге)
      • Divide and Conquer Algorithms (разделение проблемы на меньшие части и решение этих частей проблемы по-отдельности)
      • Dynamic Programming Algorithms (построение решения на основании вычисленных ранее на предыдущих шагах данных)
      • Backtracking Algorithms (перебор всех комбинаций (как и в Brute Force) с постоянной проверкой того, удовлетворяет ли текущее решение установленным ограничениям или нет, иначе — возврат на шаг назад)

      Источник

      Читайте также:  Html focus border color