# Data Structure Typed ![npm](https://img.shields.io/npm/v/data-structure-typed) ![npm](https://img.shields.io/npm/dm/data-structure-typed) ![npm package minimized gzipped size (select exports)](https://img.shields.io/bundlejs/size/data-structure-typed) ![GitHub top language](https://img.shields.io/github/languages/top/zrwusa/data-structure-typed) ![eslint](https://aleen42.github.io/badges/src/eslint.svg) ![NPM](https://img.shields.io/npm/l/data-structure-typed) [//]: # (![npm bundle size](https://img.shields.io/bundlephobia/min/data-structure-typed)) Data Structures of Javascript & TypeScript. Do you envy C++ with [STL](), 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) [//]: # (![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)) ## 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, TreeMultimap, DirectedVertex, AVLTreeNode } from 'data-structure-typed'; ``` ### CDN Copy the line below into the head tag in an HTML document. ```html ``` Copy the code below into the script tag of your HTML, and you're good to go with your development work. ```js const {Heap} = dataStructureTyped; const { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultimap, 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-multimap-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) ## Code Snippets ### 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.getNode(6); // BSTNode bst.getHeight(6) === 2; // true bst.getHeight() === 5; // true bst.getDepth(6) === 3; // true bst.getLeftMost()?.key === 1; // true bst.delete(6); bst.get(6); // undefined bst.isAVLBalanced(); // true bst.bfs()[0] === 11; // true const objBST = new BST<{height: number, age: number}>(); objBST.add(11, { "name": "Pablo", "age": 15 }); objBST.add(3, { "name": "Kirk", "age": 1 }); objBST.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5], [ { "name": "Alice", "age": 15 }, { "name": "Bob", "age": 1 }, { "name": "Charlie", "age": 8 }, { "name": "David", "age": 13 }, { "name": "Emma", "age": 16 }, { "name": "Frank", "age": 2 }, { "name": "Grace", "age": 6 }, { "name": "Hannah", "age": 9 }, { "name": "Isaac", "age": 12 }, { "name": "Jack", "age": 14 }, { "name": "Katie", "age": 4 }, { "name": "Liam", "age": 7 }, { "name": "Mia", "age": 10 }, { "name": "Noah", "age": 5 } ] ); objBST.delete(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.getNode(6); bst.getHeight(6) === 2; // true bst.getHeight() === 5; // true bst.getDepth(6) === 3; // true const leftMost = bst.getLeftMost(); leftMost?.key === 1; // true bst.delete(6); bst.get(6); // undefined bst.isAVLBalanced(); // true or false const bfsIDs = bst.bfs(); bfsIDs[0] === 11; // 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.delete(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.delete(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.deleteEdgeSrcToDest('A', 'B'); graph.hasEdge('A', 'B'); // false graph.addVertex('C'); graph.addEdge('A', 'B'); graph.addEdge('B', 'C'); const topologicalOrderKeys = 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.deleteVertex('C'); graph.addEdge('A', 'B'); graph.addEdge('B', 'D'); const dijkstraResult = graph.dijkstra('A'); Array.from(dijkstraResult?.seen ?? []).map(vertex => vertex.key) // ['A', 'B', 'D'] ``` ## Built-in classic algorithms

Algorithm	Function Description	Iteration Type
Binary Tree DFS	Traverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree, and then the right subtree, using recursion.	Recursion + Iteration
Binary Tree BFS	Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level from left to right.	Iteration
Graph DFS	Traverse a graph in a depth-first manner, starting from a given node, exploring along one path as deeply as possible, and backtracking to explore other paths. Used for finding connected components, paths, etc.	Recursion + Iteration
Binary Tree Morris	Morris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree traversal without additional stack or recursion.	Iteration
Graph BFS	Traverse a graph in a breadth-first manner, starting from a given node, first visiting nodes directly connected to the starting node, and then expanding level by level. Used for finding shortest paths, etc.	Recursion + Iteration
Graph Tarjan's Algorithm	Find strongly connected components in a graph, typically implemented using depth-first search.	Recursion
Graph Bellman-Ford Algorithm	Finding the shortest paths from a single source, can handle negative weight edges	Iteration
Graph Dijkstra's Algorithm	Finding the shortest paths from a single source, cannot handle negative weight edges	Iteration
Graph Floyd-Warshall Algorithm	Finding the shortest paths between all pairs of nodes	Iteration
Graph getCycles	Find all cycles in a graph or detect the presence of cycles.	Recursion
Graph getCutVertexes	Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph.	Recursion
Graph getSCCs	Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other.	Recursion
Graph getBridges	Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph.	Recursion
Graph topologicalSort	Perform topological sorting on a directed acyclic graph (DAG) to find a linear order of nodes such that all directed edges go from earlier nodes to later nodes.	Recursion

## API docs & Examples [API Docs](https://data-structure-typed-docs.vercel.app) [Live Examples](https://vivid-algorithm.vercel.app) Examples Repository ## Data Structures

Data Structure	Unit Test	Performance Test	API Documentation	Implemented
Binary Tree			Binary Tree
Binary Search Tree (BST)			BST
AVL Tree			AVLTree
Red Black Tree			RedBlackTree
Tree Multiset			TreeMultimap
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 Typed	C++ STL	java.util	Python collections
DoublyLinkedList<E>	list<T>	LinkedList<E>	deque
SinglyLinkedList<E>	-	-	-
Array<E>	vector<T>	ArrayList<E>	list
Queue<E>	queue<T>	Queue<E>	-
Deque<E>	deque<T>	-	-
PriorityQueue<E>	priority_queue<T>	PriorityQueue<E>	-
Heap<E>	priority_queue<T>	PriorityQueue<E>	heapq
Stack<E>	stack<T>	Stack<E>	-
Set<E>	set<T>	HashSet<E>	set
Map<K, V>	map<K, V>	HashMap<K, V>	dict
-	unordered_set<T>	HashSet<E>	-
HashMap<K, V>	unordered_map<K, V>	HashMap<K, V>	defaultdict
Map<K, V>	-	-	OrderedDict
BinaryTree<K, V>	-	-	-
BST<K, V>	-	-	-
TreeMultimap<K, V>	multimap<K, V>	-	-
AVLTree<E>	-	TreeSet<E>	-
AVLTree<K, V>	-	TreeMap<K, V>	-
AVLTree<E>	set	TreeSet<E>	-
Trie	-	-	-
-	multiset<T>	-	-
DirectedGraph<V, E>	-	-	-
UndirectedGraph<V, E>	-	-	-
-	unordered_multiset	-	Counter
-	-	LinkedHashSet<E>	-
-	-	LinkedHashMap<K, V>	-
-	unordered_multimap<K, V>	-	-
-	bitset<N>	-	-

## Code design ### Adhere to ES6 standard naming conventions for APIs. Standardize API conventions by using 'add' and 'delete' for element manipulation methods in all data structures. Opt for concise and clear method names, avoiding excessive length while ensuring explicit intent. ### Object-oriented programming(OOP) By strictly adhering to object-oriented design (BinaryTree -> BST -> AVLTree -> TreeMultimap), 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. ## Benchmark [//]: # (No deletion!!! Start of Replace Section)

avl-tree

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	33.60	29.76	0.00
10,000 add & delete randomly	72.39	13.81	0.01
10,000 addMany	41.06	24.35	0.00
10,000 get	28.01	35.71	0.00

binary-tree

test name	time taken (ms)	executions per sec	sample deviation
1,000 add randomly	12.64	79.09	6.90e-4
1,000 add & delete randomly	16.03	62.38	5.30e-4
1,000 addMany	10.44	95.78	0.00
1,000 get	18.19	54.98	3.11e-4
1,000 dfs	154.71	6.46	0.00
1,000 bfs	56.97	17.55	8.92e-4
1,000 morris	260.71	3.84	0.00

bst

test name	time taken (ms)	executions per sec	sample deviation
10,000 add randomly	29.57	33.81	2.75e-4
10,000 add & delete randomly	70.78	14.13	0.00
10,000 addMany	29.10	34.36	6.84e-4
10,000 get	28.75	34.78	6.05e-4

rb-tree

test name	time taken (ms)	executions per sec	sample deviation
100,000 add randomly	88.55	11.29	0.01
100,000 add & delete randomly	220.41	4.54	0.01
100,000 getNode	37.52	26.65	2.68e-4

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.10	9804.12	1.07e-6
1,000 addEdge	6.06	165.11	1.66e-4
1,000 getVertex	0.05	2.17e+4	3.48e-7
1,000 getEdge	23.26	43.00	0.00
tarjan	223.27	4.48	0.01
tarjan all	224.27	4.46	0.00
topologicalSort	179.19	5.58	0.00

heap

test name	time taken (ms)	executions per sec	sample deviation
10,000 add & pop	4.62	216.39	3.75e-5
10,000 fib add & pop	354.57	2.82	0.00

doubly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 unshift	225.57	4.43	0.02
1,000,000 unshift & shift	164.91	6.06	0.02
1,000,000 insertBefore	342.06	2.92	0.09

singly-linked-list

test name	time taken (ms)	executions per sec	sample deviation
10,000 push & pop	224.20	4.46	0.02
10,000 insertBefore	244.96	4.08	0.00

max-priority-queue

test name	time taken (ms)	executions per sec	sample deviation
10,000 refill & poll	11.45	87.32	1.74e-4

deque

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	222.74	4.49	0.08
1,000,000 shift	26.48	37.77	0.00

queue

test name	time taken (ms)	executions per sec	sample deviation
1,000,000 push	45.50	21.98	0.01
1,000,000 push & shift	80.10	12.48	0.00

trie

test name	time taken (ms)	executions per sec	sample deviation
100,000 push	56.99	17.55	0.01
100,000 getWords	98.43	10.16	0.01

[//]: # (No deletion!!! End of Replace Section)