data-structure-typed

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Brief

Data Structures of Javascript & TypeScript.

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.

install

npm

npm install data-structure-typed --save

yarn

yarn add data-structure-typed

CDN

<script src="https://cdn.jsdelivr.net/npm/data-structure-typed/umd/bundle.min.js"></script>

const {AVLTree} = dataStructureTyped;
const {Heap, MinHeap, SinglyLinkedList, Stack, AVLTreeNode, BST, Trie, DirectedGraph, DirectedVertex, TreeMultiset} = dataStructureTyped;

API docs & Examples

API Docs

Live Examples

Examples Repository

Code Snippet

Binary Search Tree (BST) snippet

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<BSTNode<{ id: number, keyA: number }>>();
    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

    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

    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

    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

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

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

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

complexities

complexities of data structures