test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 6.09 | 164.12 | 1.35e-4 |
100,000 add & poll | 34.55 | 28.94 | 6.43e-4 |
Installations
npm install @womorg/porro-impedit-suscipit
Developer Guide
Typescript
No
Module System
CommonJS
Node Version
20.17.0
NPM Version
10.8.2
Score
66.3
Supply Chain
92
Quality
85.9
Maintenance
100
Vulnerability
99.6
License
Releases
Contributors
Unable to fetch Contributors
Languages
JavaScript (100%)
Developer
womorg
Download Statistics
Total Downloads
381
Last Day
4
Last Week
54
Last Month
256
Last Year
381
GitHub Statistics
2,395 Commits
1 Branches
1 Contributors
Package Meta Information
Latest Version
4.8.120
Package Id
@womorg/porro-impedit-suscipit@4.8.120
Unpacked Size
276.16 kB
Size
130.26 kB
File Count
661
NPM Version
10.8.2
Node Version
20.17.0
Publised On
22 Sept 2024
Total Downloads
Cumulative downloads
Total Downloads
381
Last day
-50%
4
Compared to previous day
Last week
5.9%
54
Compared to previous week
Last month
156%
256
Compared to previous month
Last year
0%
381
Compared to previous year
Daily Downloads
Weekly Downloads
Monthly Downloads
Yearly Downloads
Dependencies
36
data-structure-typed
Installation and Usage
npm
1npm i data-structure-typed --save
yarn
1yarn add data-structure-typed
1import { 2 Heap, Graph, Queue, Deque, PriorityQueue, BST, Trie, DoublyLinkedList, 3 AVLTree, SinglyLinkedList, DirectedGraph, RedBlackTree, TreeMultiMap, 4 DirectedVertex, Stack, AVLTreeNode 5} from 'data-structure-typed';
If you only want to use a specific data structure independently, you can install it separately, for example, by running
1npm i heap-typed --save
Why
Do you envy C++ with STL (std::), Python with collections, and Java with java.util ? Well, no need to envy
anymore! JavaScript and TypeScript now have data-structure-typed.Benchmark
compared with C++ STL.
API standards
aligned with ES6 and Java. Usability
is comparable to Python
Performance
Performance surpasses that of native JS/TS
Method | Time Taken | Data Scale | Belongs To | big O |
---|---|---|---|---|
Queue.push & shift | 5.83 ms | 100K | Ours | O(1) |
Array.push & shift | 2829.59 ms | 100K | Native JS | O(n) |
Deque.unshift & shift | 2.44 ms | 100K | Ours | O(1) |
Array.unshift & shift | 4750.37 ms | 100K | Native JS | O(n) |
HashMap.set | 122.51 ms | 1M | Ours | O(1) |
Map.set | 223.80 ms | 1M | Native JS | O(1) |
Set.add | 185.06 ms | 1M | Native JS | O(1) |
Conciseness and uniformity
In java.utils, you need to memorize a table for all sequential data structures(Queue, Deque, LinkedList),
Java ArrayList | Java Queue | Java ArrayDeque | Java LinkedList |
---|---|---|---|
add | offer | push | push |
remove | poll | removeLast | removeLast |
remove | poll | removeFirst | removeFirst |
add(0, element) | offerFirst | unshift | unshift |
whereas in our data-structure-typed, you only need to remember four methods: push
, pop
, shift
, and unshift
for all sequential data structures(Queue, Deque, DoublyLinkedList, SinglyLinkedList and Array).
Data structures available
We provide data structures that are not available in JS/TS
Data Structure | Unit Test | Perf Test | API Doc | NPM | Downloads |
---|---|---|---|---|---|
Binary Tree | Docs | NPM | |||
Binary Search Tree (BST) | Docs | NPM | |||
AVL Tree | Docs | NPM | |||
Red Black Tree | Docs | NPM | |||
Tree Multimap | Docs | NPM | |||
Heap | Docs | NPM | |||
Priority Queue | Docs | NPM | |||
Max Priority Queue | Docs | NPM | |||
Min Priority Queue | Docs | NPM | |||
Trie | Docs | NPM | |||
Graph | Docs | NPM | |||
Directed Graph | Docs | NPM | |||
Undirected Graph | Docs | NPM | |||
Queue | Docs | NPM | |||
Deque | Docs | NPM | |||
Hash Map | Docs | ||||
Linked List | Docs | NPM | |||
Singly Linked List | Docs | NPM | |||
Doubly Linked List | Docs | NPM | |||
Stack | Docs | NPM | |||
Segment Tree | Docs | ||||
Binary Indexed Tree | Docs |
Vivid Examples
AVL Tree
Try it out, or you can run your own code using our visual tool
Tree Multi Map
Directed Graph
Map Graph
Code Snippets
Red Black Tree snippet
TS
1import { RedBlackTree } from 'data-structure-typed'; 2 3const rbTree = new RedBlackTree<number>(); 4rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) 5rbTree.isAVLBalanced(); // true 6rbTree.delete(10); 7rbTree.isAVLBalanced(); // true 8rbTree.print() 9// ___6________ 10// / \ 11// ___4_ ___11________ 12// / \ / \ 13// _2_ 5 _8_ ____14__ 14// / \ / \ / \ 15// 1 3 7 9 12__ 15__ 16// \ \ 17// 13 16
JS
1import { RedBlackTree } from 'data-structure-typed'; 2 3const rbTree = new RedBlackTree(); 4rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) 5rbTree.isAVLBalanced(); // true 6rbTree.delete(10); 7rbTree.isAVLBalanced(); // true 8rbTree.print() 9// ___6________ 10// / \ 11// ___4_ ___11________ 12// / \ / \ 13// _2_ 5 _8_ ____14__ 14// / \ / \ / \ 15// 1 3 7 9 12__ 15__ 16// \ \ 17// 13 16
Free conversion between data structures.
1const orgArr = [6, 1, 2, 7, 5, 3, 4, 9, 8]; 2const orgStrArr = ["trie", "trial", "trick", "trip", "tree", "trend", "triangle", "track", "trace", "transmit"]; 3const entries = [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]]; 4 5const queue = new Queue(orgArr); 6queue.print(); 7// [6, 1, 2, 7, 5, 3, 4, 9, 8] 8 9const deque = new Deque(orgArr); 10deque.print(); 11// [6, 1, 2, 7, 5, 3, 4, 9, 8] 12 13const sList = new SinglyLinkedList(orgArr); 14sList.print(); 15// [6, 1, 2, 7, 5, 3, 4, 9, 8] 16 17const dList = new DoublyLinkedList(orgArr); 18dList.print(); 19// [6, 1, 2, 7, 5, 3, 4, 9, 8] 20 21const stack = new Stack(orgArr); 22stack.print(); 23// [6, 1, 2, 7, 5, 3, 4, 9, 8] 24 25const minHeap = new MinHeap(orgArr); 26minHeap.print(); 27// [1, 5, 2, 7, 6, 3, 4, 9, 8] 28 29const maxPQ = new MaxPriorityQueue(orgArr); 30maxPQ.print(); 31// [9, 8, 4, 7, 5, 2, 3, 1, 6] 32 33const biTree = new BinaryTree(entries); 34biTree.print(); 35// ___6___ 36// / \ 37// ___1_ _2_ 38// / \ / \ 39// _7_ 5 3 4 40// / \ 41// 9 8 42 43const bst = new BST(entries); 44bst.print(); 45// _____5___ 46// / \ 47// _2_ _7_ 48// / \ / \ 49// 1 3_ 6 8_ 50// \ \ 51// 4 9 52 53 54const rbTree = new RedBlackTree(entries); 55rbTree.print(); 56// ___4___ 57// / \ 58// _2_ _6___ 59// / \ / \ 60// 1 3 5 _8_ 61// / \ 62// 7 9 63 64 65const avl = new AVLTree(entries); 66avl.print(); 67// ___4___ 68// / \ 69// _2_ _6___ 70// / \ / \ 71// 1 3 5 _8_ 72// / \ 73// 7 9 74 75const treeMulti = new TreeMultiMap(entries); 76treeMulti.print(); 77// ___4___ 78// / \ 79// _2_ _6___ 80// / \ / \ 81// 1 3 5 _8_ 82// / \ 83// 7 9 84 85const hm = new HashMap(entries); 86hm.print() 87// [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]] 88 89const rbTreeH = new RedBlackTree(hm); 90rbTreeH.print(); 91// ___4___ 92// / \ 93// _2_ _6___ 94// / \ / \ 95// 1 3 5 _8_ 96// / \ 97// 7 9 98 99const pq = new MinPriorityQueue(orgArr); 100pq.print(); 101// [1, 5, 2, 7, 6, 3, 4, 9, 8] 102 103const bst1 = new BST(pq); 104bst1.print(); 105// _____5___ 106// / \ 107// _2_ _7_ 108// / \ / \ 109// 1 3_ 6 8_ 110// \ \ 111// 4 9 112 113const dq1 = new Deque(orgArr); 114dq1.print(); 115// [6, 1, 2, 7, 5, 3, 4, 9, 8] 116const rbTree1 = new RedBlackTree(dq1); 117rbTree1.print(); 118// _____5___ 119// / \ 120// _2___ _7___ 121// / \ / \ 122// 1 _4 6 _9 123// / / 124// 3 8 125 126 127const trie2 = new Trie(orgStrArr); 128trie2.print(); 129// ['trie', 'trial', 'triangle', 'trick', 'trip', 'tree', 'trend', 'track', 'trace', 'transmit'] 130const heap2 = new Heap(trie2, { comparator: (a, b) => Number(a) - Number(b) }); 131heap2.print(); 132// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle'] 133const dq2 = new Deque(heap2); 134dq2.print(); 135// ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle'] 136const entries2 = dq2.map((el, i) => [i, el]); 137const avl2 = new AVLTree(entries2); 138avl2.print(); 139// ___3_______ 140// / \ 141// _1_ ___7_ 142// / \ / \ 143// 0 2 _5_ 8_ 144// / \ \ 145// 4 6 9
Binary Search Tree (BST) snippet
1import { BST, BSTNode } from 'data-structure-typed'; 2 3const bst = new BST<number>(); 4bst.add(11); 5bst.add(3); 6bst.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]); 7bst.size === 16; // true 8bst.has(6); // true 9const node6 = bst.getNode(6); // BSTNode 10bst.getHeight(6) === 2; // true 11bst.getHeight() === 5; // true 12bst.getDepth(6) === 3; // true 13 14bst.getLeftMost()?.key === 1; // true 15 16bst.delete(6); 17bst.get(6); // undefined 18bst.isAVLBalanced(); // true 19bst.bfs()[0] === 11; // true 20bst.print() 21// ______________11_____ 22// / \ 23// ___3_______ _13_____ 24// / \ / \ 25// 1_ _____8____ 12 _15__ 26// \ / \ / \ 27// 2 4_ _10 14 16 28// \ / 29// 5_ 9 30// \ 31// 7 32 33const objBST = new BST<number, { height: number, age: number }>(); 34 35objBST.add(11, { "name": "Pablo", "age": 15 }); 36objBST.add(3, { "name": "Kirk", "age": 1 }); 37 38objBST.addMany([15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5], [ 39 { "name": "Alice", "age": 15 }, 40 { "name": "Bob", "age": 1 }, 41 { "name": "Charlie", "age": 8 }, 42 { "name": "David", "age": 13 }, 43 { "name": "Emma", "age": 16 }, 44 { "name": "Frank", "age": 2 }, 45 { "name": "Grace", "age": 6 }, 46 { "name": "Hannah", "age": 9 }, 47 { "name": "Isaac", "age": 12 }, 48 { "name": "Jack", "age": 14 }, 49 { "name": "Katie", "age": 4 }, 50 { "name": "Liam", "age": 7 }, 51 { "name": "Mia", "age": 10 }, 52 { "name": "Noah", "age": 5 } 53 ] 54); 55 56objBST.delete(11);
AVLTree snippet
1import { AVLTree } from 'data-structure-typed'; 2 3const avlTree = new AVLTree<number>(); 4avlTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) 5avlTree.isAVLBalanced(); // true 6avlTree.delete(10); 7avlTree.isAVLBalanced(); // true
Directed Graph simple snippet
1import { DirectedGraph } from 'data-structure-typed'; 2 3const graph = new DirectedGraph<string>(); 4 5graph.addVertex('A'); 6graph.addVertex('B'); 7 8graph.hasVertex('A'); // true 9graph.hasVertex('B'); // true 10graph.hasVertex('C'); // false 11 12graph.addEdge('A', 'B'); 13graph.hasEdge('A', 'B'); // true 14graph.hasEdge('B', 'A'); // false 15 16graph.deleteEdgeSrcToDest('A', 'B'); 17graph.hasEdge('A', 'B'); // false 18 19graph.addVertex('C'); 20 21graph.addEdge('A', 'B'); 22graph.addEdge('B', 'C'); 23 24const topologicalOrderKeys = graph.topologicalSort(); // ['A', 'B', 'C']
Undirected Graph snippet
1import { UndirectedGraph } from 'data-structure-typed'; 2 3const graph = new UndirectedGraph<string>(); 4graph.addVertex('A'); 5graph.addVertex('B'); 6graph.addVertex('C'); 7graph.addVertex('D'); 8graph.deleteVertex('C'); 9graph.addEdge('A', 'B'); 10graph.addEdge('B', 'D'); 11 12const dijkstraResult = graph.dijkstra('A'); 13Array.from(dijkstraResult?.seen ?? []).map(vertex => vertex.key) // ['A', 'B', 'D'] 14 15
API docs & Examples
Benchmark
MacBook Pro (15-inch, 2018)
Processor 2.2 GHz 6-Core Intel Core i7
Memory 16 GB 2400 MHz DDR4
Graphics Radeon Pro 555X 4 GB
Intel UHD Graphics 630 1536 MB
macOS Big Sur
Version 11.7.9
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 76.73 | 13.03 | 0.00 |
100,000 add randomly | 80.67 | 12.40 | 0.00 |
100,000 get | 110.86 | 9.02 | 0.00 |
100,000 iterator | 24.99 | 40.02 | 0.00 |
100,000 add & delete orderly | 152.66 | 6.55 | 0.00 |
100,000 add & delete randomly | 230.75 | 4.33 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 39.27 | 25.46 | 0.01 |
100,000 push & shift | 4.53 | 220.81 | 4.84e-4 |
Native JS Array 100,000 push & shift | 1948.05 | 0.51 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 23.22 | 43.06 | 0.00 |
1,000,000 push & pop | 29.68 | 33.69 | 0.00 |
1,000,000 push & shift | 29.33 | 34.09 | 0.00 |
100,000 push & shift | 3.10 | 323.01 | 2.47e-4 |
Native JS Array 100,000 push & shift | 1942.12 | 0.51 | 0.02 |
100,000 unshift & shift | 2.77 | 360.50 | 2.43e-4 |
Native JS Array 100,000 unshift & shift | 3835.21 | 0.26 | 0.03 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 set | 112.38 | 8.90 | 0.02 |
Native JS Map 1,000,000 set | 199.97 | 5.00 | 0.01 |
Native JS Set 1,000,000 add | 163.34 | 6.12 | 0.01 |
1,000,000 set & get | 109.86 | 9.10 | 0.02 |
Native JS Map 1,000,000 set & get | 255.33 | 3.92 | 0.00 |
Native JS Set 1,000,000 add & has | 163.91 | 6.10 | 0.00 |
1,000,000 ObjKey set & get | 317.89 | 3.15 | 0.04 |
Native JS Map 1,000,000 ObjKey set & get | 282.99 | 3.53 | 0.03 |
Native JS Set 1,000,000 ObjKey add & has | 253.93 | 3.94 | 0.03 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 push | 43.71 | 22.88 | 7.33e-4 |
100,000 getWords | 83.63 | 11.96 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 271.93 | 3.68 | 0.01 |
100,000 add randomly | 318.27 | 3.14 | 0.00 |
100,000 get | 128.85 | 7.76 | 0.00 |
100,000 iterator | 29.09 | 34.38 | 0.00 |
100,000 add & delete orderly | 435.48 | 2.30 | 7.44e-4 |
100,000 add & delete randomly | 578.70 | 1.73 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 RBTree add randomly | 6.69 | 149.54 | 1.06e-4 |
10,000 RBTree get randomly | 9.19 | 108.82 | 1.43e-4 |
10,000 RBTree add & delete randomly | 18.54 | 53.94 | 1.73e-4 |
10,000 AVLTree add randomly | 23.70 | 42.20 | 1.88e-4 |
10,000 AVLTree get randomly | 9.89 | 101.11 | 0.00 |
10,000 AVLTree add & delete randomly | 44.44 | 22.50 | 4.30e-4 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000 addVertex | 0.10 | 9766.65 | 9.83e-7 |
1,000 addEdge | 6.15 | 162.57 | 7.99e-4 |
1,000 getVertex | 0.05 | 2.18e+4 | 4.52e-7 |
1,000 getEdge | 22.70 | 44.06 | 0.00 |
tarjan | 203.00 | 4.93 | 0.01 |
topologicalSort | 176.40 | 5.67 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 222.02 | 4.50 | 0.07 |
1,000,000 unshift | 220.41 | 4.54 | 0.05 |
1,000,000 unshift & shift | 185.31 | 5.40 | 0.01 |
1,000,000 addBefore | 317.20 | 3.15 | 0.07 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push & shift | 204.82 | 4.88 | 0.09 |
10,000 push & pop | 221.88 | 4.51 | 0.03 |
10,000 addBefore | 247.28 | 4.04 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 26.97 | 37.08 | 7.97e-4 |
100,000 add & poll | 74.55 | 13.41 | 5.19e-4 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 35.54 | 28.14 | 0.00 |
1,000,000 push & pop | 44.89 | 22.27 | 0.01 |
The corresponding relationships between data structures in different language standard libraries.
Data Structure Typed | C++ STL | java.util | Python collections |
---|---|---|---|
Heap<E> | - | - | heapq |
PriorityQueue<E> | priority_queue<T> | PriorityQueue<E> | - |
Deque<E> | deque<T> | ArrayDeque<E> | deque |
Queue<E> | queue<T> | Queue<E> | - |
HashMap<K, V> | unordered_map<K, V> | HashMap<K, V> | defaultdict |
DoublyLinkedList<E> | list<T> | LinkedList<E> | - |
SinglyLinkedList<E> | - | - | - |
BinaryTree<K, V> | - | - | - |
BST<K, V> | - | - | - |
RedBlackTree<E> | set<T> | TreeSet<E> | - |
RedBlackTree<K, V> | map<K, V> | TreeMap<K, V> | - |
TreeMultiMap<K, V> | multimap<K, V> | - | - |
TreeMultiMap<E> | multiset<T> | - | - |
Trie | - | - | - |
DirectedGraph<V, E> | - | - | - |
UndirectedGraph<V, E> | - | - | - |
PriorityQueue<E> | priority_queue<T> | PriorityQueue<E> | - |
Array<E> | vector<T> | ArrayList<E> | list |
Stack<E> | stack<T> | Stack<E> | - |
HashMap<E> | unordered_set<T> | HashSet<E> | set |
- | unordered_multiset | - | Counter |
LinkedHashMap<K, V> | - | LinkedHashMap<K, V> | OrderedDict |
- | unordered_multimap<K, V> | - | - |
- | bitset<N> | - | - |
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 getCutVertices | 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 |
Software Engineering Design Standards
We strictly adhere to computer science theory and software development standards. Our LinkedList is designed in the traditional sense of the LinkedList data structure, and we refrain from substituting it with a Deque solely for the purpose of showcasing performance test data. However, we have also implemented a Deque based on a dynamic array concurrently.
Principle | Description |
---|---|
Practicality | Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names. |
Extensibility | Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures. |
Modularization | Includes data structure modularization and independent NPM packages. |
Efficiency | All methods provide time and space complexity, comparable to native JS performance. |
Maintainability | Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns. |
Testability | Automated and customized unit testing, performance testing, and integration testing. |
Portability | Plans for porting to Java, Python, and C++, currently achieved to 80%. |
Reusability | Fully decoupled, minimized side effects, and adheres to OOP. |
Security | Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects. |
Scalability | Data structure software does not involve load issues. |
supported module system
Now you can use it in Node.js and browser environments
CommonJS:require export.modules =
ESModule:Â Â Â import export
Typescript:Â Â Â import export
UMD:Â Â Â Â Â Â Â Â Â Â Â var Deque = dataStructureTyped.Deque
CDN
Copy the line below into the head tag in an HTML document.
development
1 2<script src='https://cdn.jsdelivr.net/npm/data-structure-typed/dist/umd/data-structure-typed.js'></script>
production
1 2<script src='https://cdn.jsdelivr.net/npm/data-structure-typed/dist/umd/data-structure-typed.min.js'></script>
Copy the code below into the script tag of your HTML, and you're good to go with your development.
1const { Heap } = dataStructureTyped; 2const { 3 BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, 4 AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiMap, 5 DirectedVertex, AVLTreeNode 6} = dataStructureTyped;
No vulnerabilities found.
No security vulnerabilities found.