# Data Structure Typed Data Structures of Javascript & TypeScript. Do you envy C++ with [std](), Python with [collections](), and Java with [java.util]() ? Well, no need to envy anymore! JavaScript and TypeScript now have [data-structure-typed](). Now you can use this library in Node.js and browser environments in CommonJS(require export.modules = ), ESModule(import export), Typescript(import export), UMD(var Queue = dataStructureTyped.Queue) The size after packaging is 69 kB. ![License](https://img.shields.io/badge/License-MIT-blue.svg) ![Language](https://img.shields.io/github/languages/top/zrwusa/data-structure-typed) ![GitHub release (latest by date)](https://img.shields.io/github/v/release/zrwusa/data-structure-typed) ![npm](https://aleen42.github.io/badges/src/npm.svg) ![eslint](https://aleen42.github.io/badges/src/eslint.svg) [//]: # (![Branches](https://img.shields.io/badge/branches-55.47%25-red.svg?style=flat)) [//]: # (![Statements](https://img.shields.io/badge/statements-67%25-red.svg?style=flat)) [//]: # (![Functions](https://img.shields.io/badge/functions-66.38%25-red.svg?style=flat)) [//]: # (![Lines](https://img.shields.io/badge/lines-68.6%25-red.svg?style=flat)) ## Built-in classic algorithms DFS(Depth-First Search), DFSIterative, BFS(Breadth-First Search), morris, Bellman-Ford Algorithm, Dijkstra's Algorithm, Floyd-Warshall Algorithm, Tarjan's Algorithm. ## Installation and Usage ### npm ```bash npm i data-structure-typed ``` ### yarn ```bash yarn add data-structure-typed ``` ```js import { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiset, DirectedVertex, AVLTreeNode } from 'data-structure-typed'; ``` ### CDN ```html ``` ```js const {Heap} = dataStructureTyped; const { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiset, DirectedVertex, AVLTreeNode } = dataStructureTyped; ``` ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-array-to-binary-tree.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-dfs-in-order.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/avl-tree-test.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/tree-multiset-test.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/matrix-cut-off-tree-for-golf.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/directed-graph-test.webp) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/map-graph-test.webp) ## API docs & Examples [API Docs](https://data-structure-typed-docs.vercel.app) [Live Examples](https://vivid-algorithm.vercel.app) Examples Repository ## Code Snippet ### Binary Search Tree (BST) snippet #### TS ```ts import {BST, BSTNode} from 'data-structure-typed'; const bst = new BST(); bst.add(11); bst.add(3); bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]); bst.size === 16; // true bst.has(6); // true const node6 = bst.get(6); // BSTNode bst.getHeight(6) === 2; // true bst.getHeight() === 5; // true bst.getDepth(6) === 3; // true bst.getLeftMost()?.id === 1; // true bst.remove(6); bst.get(6); // null bst.isAVLBalanced(); // true bst.BFS()[0] === 11; // true const objBST = new BST>(); objBST.add(11, {id: 11, keyA: 11}); objBST.add(3, {id: 3, keyA: 3}); objBST.addMany([{id: 15, keyA: 15}, {id: 1, keyA: 1}, {id: 8, keyA: 8}, {id: 13, keyA: 13}, {id: 16, keyA: 16}, {id: 2, keyA: 2}, {id: 6, keyA: 6}, {id: 9, keyA: 9}, {id: 12, keyA: 12}, {id: 14, keyA: 14}, {id: 4, keyA: 4}, {id: 7, keyA: 7}, {id: 10, keyA: 10}, {id: 5, keyA: 5}]); objBST.remove(11); ``` #### JS ```js const {BST, BSTNode} = require('data-structure-typed'); const bst = new BST(); bst.add(11); bst.add(3); bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]); bst.size === 16; // true bst.has(6); // true const node6 = bst.get(6); bst.getHeight(6) === 2; // true bst.getHeight() === 5; // true bst.getDepth(6) === 3; // true const leftMost = bst.getLeftMost(); leftMost?.id === 1; // true expect(leftMost?.id).toBe(1); bst.remove(6); bst.get(6); // null bst.isAVLBalanced(); // true or false const bfsIDs = bst.BFS(); bfsIDs[0] === 11; // true expect(bfsIDs[0]).toBe(11); const objBST = new BST(); objBST.add(11, {id: 11, keyA: 11}); objBST.add(3, {id: 3, keyA: 3}); objBST.addMany([{id: 15, keyA: 15}, {id: 1, keyA: 1}, {id: 8, keyA: 8}, {id: 13, keyA: 13}, {id: 16, keyA: 16}, {id: 2, keyA: 2}, {id: 6, keyA: 6}, {id: 9, keyA: 9}, {id: 12, keyA: 12}, {id: 14, keyA: 14}, {id: 4, keyA: 4}, {id: 7, keyA: 7}, {id: 10, keyA: 10}, {id: 5, keyA: 5}]); objBST.remove(11); const avlTree = new AVLTree(); avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) avlTree.isAVLBalanced(); // true avlTree.remove(10); avlTree.isAVLBalanced(); // true ``` ### AVLTree snippet #### TS ```ts import {AVLTree} from 'data-structure-typed'; const avlTree = new AVLTree(); avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) avlTree.isAVLBalanced(); // true avlTree.remove(10); avlTree.isAVLBalanced(); // true ``` #### JS ```js const {AVLTree} = require('data-structure-typed'); const avlTree = new AVLTree(); avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) avlTree.isAVLBalanced(); // true avlTree.remove(10); avlTree.isAVLBalanced(); // true ``` ### Directed Graph simple snippet #### TS or JS ```ts import {DirectedGraph} from 'data-structure-typed'; const graph = new DirectedGraph(); graph.addVertex('A'); graph.addVertex('B'); graph.hasVertex('A'); // true graph.hasVertex('B'); // true graph.hasVertex('C'); // false graph.addEdge('A', 'B'); graph.hasEdge('A', 'B'); // true graph.hasEdge('B', 'A'); // false graph.removeEdgeSrcToDest('A', 'B'); graph.hasEdge('A', 'B'); // false graph.addVertex('C'); graph.addEdge('A', 'B'); graph.addEdge('B', 'C'); const topologicalOrderIds = graph.topologicalSort(); // ['A', 'B', 'C'] ``` ### Undirected Graph snippet #### TS or JS ```ts import {UndirectedGraph} from 'data-structure-typed'; const graph = new UndirectedGraph(); graph.addVertex('A'); graph.addVertex('B'); graph.addVertex('C'); graph.addVertex('D'); graph.removeVertex('C'); graph.addEdge('A', 'B'); graph.addEdge('B', 'D'); const dijkstraResult = graph.dijkstra('A'); Array.from(dijkstraResult?.seen ?? []).map(vertex => vertex.id) // ['A', 'B', 'D'] ``` ## Data Structures
Data Structure Unit Test Performance Test API Documentation Implemented
Binary Tree Binary Tree
Binary Search Tree (BST) BST
AVL Tree AVLTree
Tree Multiset TreeMultiset
Segment Tree SegmentTree
Binary Indexed Tree BinaryIndexedTree
Graph AbstractGraph
Directed Graph DirectedGraph
Undirected Graph UndirectedGraph
Linked List SinglyLinkedList
Singly Linked List SinglyLinkedList
Doubly Linked List DoublyLinkedList
Queue Queue
Object Deque ObjectDeque
Array Deque ArrayDeque
Stack Stack
Coordinate Set CoordinateSet
Coordinate Map CoordinateMap
Heap Heap
Priority Queue PriorityQueue
Max Priority Queue MaxPriorityQueue
Min Priority Queue MinPriorityQueue
Trie Trie
### Standard library data structure comparison
Data Structure C++ std Data Structure Typed java.util Python collections
Dynamic Array std::vector<T> Array<E> ArrayList<E> list
Linked List std::list<T> DoublyLinkedList<E> LinkedList<E> deque
Set std::set<T> Set<E> HashSet<E> set
Map std::map<K, V> Map<K, V> HashMap<K, V> dict
Unordered Set std::unordered_set<T> N/A HashSet<E> N/A
Unordered Map std::unordered_map<K, V> HashMap<K, V> HashMap<K, V> defaultdict
Queue std::queue<T> Queue<E> Queue<E> N/A
Priority Queue std::priority_queue<T> PriorityQueue<E> PriorityQueue<E> N/A
Stack std::stack<T> Stack<E> Stack<E> N/A
Bitset std::bitset<N> N/A N/A N/A
Deque std::deque<T> Deque<E> N/A N/A
Multiset std::multiset<T> N/A N/A N/A
Multimap std::multimap<K, V> N/A N/A N/A
Unordered Multiset std::unordered_multiset N/A Counter N/A
Ordered Dictionary N/A Map<K, V> N/A OrderedDict
Linked Hash Set N/A N/A LinkedHashSet<E> N/A
Linked Hash Map N/A N/A LinkedHashMap<K, V> N/A
Sorted Set N/A AVLTree TreeSet<E> N/A
Sorted Map N/A AVLTree TreeMap<K, V> N/A
Tree Set std::set AVLTree TreeSet<E> N/A
Persistent Collections N/A N/A N/A N/A
unordered multiset unordered multiset<T> N/A N/A N/A
Unordered Multimap std::unordered_multimap<K, V> N/A N/A N/A
## Code design By strictly adhering to object-oriented design (BinaryTree -> BST -> AVLTree -> TreeMultiset), you can seamlessly inherit the existing data structures to implement the customized ones you need. Object-oriented design stands as the optimal approach to data structure design. ## Complexities ### performance of Big O
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 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
### Sorting Complexity
Name Best Average Worst Memory Stable Comments
Bubble sort n n2 n2 1 Yes
Insertion sort n n2 n2 1 Yes
Selection sort n2 n2 n2 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) n2 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