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) ## 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 [//]: # (![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)) ### Data structures available We provide data structures that are not available in JS/TS Heap, Binary Tree, Red Black Tree, Linked List, Deque, Trie, Directed Graph, Undirected Graph, BST, AVL Tree, Priority Queue, Queue, Tree Multiset. ### Performance Performance surpasses that of native JS/TS
Method Time Taken Data Scale Belongs To Complexity
Queue.push & shift 5.83 ms 100,000 Ours O(1)
Array.push & shift 2829.59 ms 100,000 Native JS O(n)
Deque.unshift & shift 2.44 ms 100,000 Ours O(1)
Array.unshift & shift 4750.37 ms 100,000 Native JS O(n)
HashMap.set 122.51 ms 1,000,000 Ours O(1)
Map.set 223.80 ms 1,000,000 Native JS O(1)
Set.add 185.06 ms 1,000,000 Native JS O(1)
### Conciseness and uniformity In Java.utils, you need to memorize a table for all sequential data structures(Queue, Deque, LinkedList),
Operation Java ArrayList Java Queue Java ArrayDeque Java LinkedList
push add offer push push
pop remove poll removeLast removeLast
shift remove poll removeFirst removeFirst
unshift 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. ## Installation and Usage ### npm ```bash npm i data-structure-typed --save ``` ### yarn ```bash yarn add data-structure-typed ``` ```js import { Heap, Graph, Queue, Deque, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, SinglyLinkedList, DirectedGraph, RedBlackTree, TreeMultiMap, DirectedVertex, Stack, AVLTreeNode } from 'data-structure-typed'; ``` ## Vivid Examples ### AVL Tree [Try it out](https://vivid-algorithm.vercel.app/), or you can run your own code using our [visual tool](https://github.com/zrwusa/vivid-algorithm) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/avl-tree-test.webp) ### Tree Multi Map [Try it out](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/tree-multiset-test.webp) ### Directed Graph [Try it out](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/directed-graph-test.webp) ### Map Graph [Try it out](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/map-graph-test.webp) ## Code Snippets ### Red Black Tree snippet #### TS ```ts import { RedBlackTree } from 'data-structure-typed'; const rbTree = new RedBlackTree(); rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) rbTree.isAVLBalanced(); // true rbTree.delete(10); rbTree.isAVLBalanced(); // true rbTree.print() // ___6________ // / \ // ___4_ ___11________ // / \ / \ // _2_ 5 _8_ ____14__ // / \ / \ / \ // 1 3 7 9 12__ 15__ // \ \ // 13 16 ``` #### JS ```js import { RedBlackTree } from 'data-structure-typed'; const rbTree = new RedBlackTree(); rbTree.addMany([11, 3, 15, 1, 8, 13, 16, 2, 6, 9, 12, 14, 4, 7, 10, 5]) rbTree.isAVLBalanced(); // true rbTree.delete(10); rbTree.isAVLBalanced(); // true rbTree.print() // ___6________ // / \ // ___4_ ___11________ // / \ / \ // _2_ 5 _8_ ____14__ // / \ / \ / \ // 1 3 7 9 12__ 15__ // \ \ // 13 16 ``` ### Free conversion between data structures. ```js const orgArr = [6, 1, 2, 7, 5, 3, 4, 9, 8]; const orgStrArr = ["trie", "trial", "trick", "trip", "tree", "trend", "triangle", "track", "trace", "transmit"]; const entries = [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]]; const queue = new Queue(orgArr); queue.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const deque = new Deque(orgArr); deque.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const sList = new SinglyLinkedList(orgArr); sList.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const dList = new DoublyLinkedList(orgArr); dList.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const stack = new Stack(orgArr); stack.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const minHeap = new MinHeap(orgArr); minHeap.print(); // [1, 5, 2, 7, 6, 3, 4, 9, 8] const maxPQ = new MaxPriorityQueue(orgArr); maxPQ.print(); // [9, 8, 4, 7, 5, 2, 3, 1, 6] const biTree = new BinaryTree(entries); biTree.print(); // ___6___ // / \ // ___1_ _2_ // / \ / \ // _7_ 5 3 4 // / \ // 9 8 const bst = new BST(entries); bst.print(); // _____5___ // / \ // _2_ _7_ // / \ / \ // 1 3_ 6 8_ // \ \ // 4 9 const rbTree = new RedBlackTree(entries); rbTree.print(); // ___4___ // / \ // _2_ _6___ // / \ / \ // 1 3 5 _8_ // / \ // 7 9 const avl = new AVLTree(entries); avl.print(); // ___4___ // / \ // _2_ _6___ // / \ / \ // 1 3 5 _8_ // / \ // 7 9 const treeMulti = new TreeMultiMap(entries); treeMulti.print(); // ___4___ // / \ // _2_ _6___ // / \ / \ // 1 3 5 _8_ // / \ // 7 9 const hm = new HashMap(entries); hm.print() // [[6, "6"], [1, "1"], [2, "2"], [7, "7"], [5, "5"], [3, "3"], [4, "4"], [9, "9"], [8, "8"]] const rbTreeH = new RedBlackTree(hm); rbTreeH.print(); // ___4___ // / \ // _2_ _6___ // / \ / \ // 1 3 5 _8_ // / \ // 7 9 const pq = new MinPriorityQueue(orgArr); pq.print(); // [1, 5, 2, 7, 6, 3, 4, 9, 8] const bst1 = new BST(pq); bst1.print(); // _____5___ // / \ // _2_ _7_ // / \ / \ // 1 3_ 6 8_ // \ \ // 4 9 const dq1 = new Deque(orgArr); dq1.print(); // [6, 1, 2, 7, 5, 3, 4, 9, 8] const rbTree1 = new RedBlackTree(dq1); rbTree1.print(); // _____5___ // / \ // _2___ _7___ // / \ / \ // 1 _4 6 _9 // / / // 3 8 const trie2 = new Trie(orgStrArr); trie2.print(); // ['trie', 'trial', 'triangle', 'trick', 'trip', 'tree', 'trend', 'track', 'trace', 'transmit'] const heap2 = new Heap(trie2, { comparator: (a, b) => Number(a) - Number(b) }); heap2.print(); // ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle'] const dq2 = new Deque(heap2); dq2.print(); // ['transmit', 'trace', 'tree', 'trend', 'track', 'trial', 'trip', 'trie', 'trick', 'triangle'] const entries2 = dq2.map((el, i) => [i, el]); const avl2 = new AVLTree(entries2); avl2.print(); // ___3_______ // / \ // _1_ ___7_ // / \ / \ // 0 2 _5_ 8_ // / \ \ // 4 6 9 ``` ### 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.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 bst.print() // ______________11_____ // / \ // ___3_______ _13_____ // / \ / \ // 1_ _____8____ 12 _15__ // \ / \ / \ // 2 4_ _10 14 16 // \ / // 5_ 9 // \ // 7 const objBST = new BST(); 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); ``` ### 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.delete(10); avlTree.isAVLBalanced(); // true ``` ### Directed Graph simple snippet ```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 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'] ``` ## 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 Docs
Binary Tree View
Binary Search Tree (BST) View
AVL Tree View
Red Black Tree View
Tree Multimap View
Heap View
Priority Queue View
Max Priority Queue View
Min Priority Queue View
Trie View
Graph View
Directed Graph View
Undirected Graph View
Queue View
Deque View
Hash Map View
Linked List View
Singly Linked List View
Doubly Linked List View
Stack View
Segment Tree View
Binary Indexed Tree View
## 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.
## Benchmark macOS Big Sur Version 11.7.9 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 [//]: # (No deletion!!! Start of Replace Section)
heap
test nametime taken (ms)executions per secsample deviation
100,000 add6.42155.871.90e-4
100,000 add & poll31.5031.748.72e-4
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add orderly42.4623.550.01
100,000 delete orderly176.045.680.01
100,000 add randomly98.0210.200.00
100,000 delete randomly182.585.480.00
100,000 add orderly42.1023.768.79e-4
100,000 delete randomly182.165.495.65e-4
100,000 getNode randomly180.635.540.00
100,000 add & iterator129.927.700.00
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push44.3322.560.01
100,000 push & shift4.68213.631.41e-4
Native JS Array 100,000 push & shift2633.110.380.44
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push24.6240.620.00
1,000,000 push & pop30.7132.560.00
1,000,000 push & shift31.9331.320.01
100,000 push & shift3.25307.222.74e-4
Native JS Array 100,000 push & shift2455.510.410.29
100,000 unshift & shift2.94340.312.39e-4
Native JS Array 100,000 unshift & shift4314.200.230.41
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set116.608.580.02
Native JS Map 1,000,000 set202.394.940.01
Native JS Set 1,000,000 add167.895.960.01
1,000,000 set & get125.217.990.03
Native JS Map 1,000,000 set & get272.203.670.03
Native JS Set 1,000,000 add & has168.515.930.01
1,000,000 ObjKey set & get309.103.240.01
Native JS Map 1,000,000 ObjKey set & get299.433.340.03
Native JS Set 1,000,000 ObjKey add & has260.933.830.02
trie
test nametime taken (ms)executions per secsample deviation
100,000 push43.3223.086.16e-4
100,000 getWords84.8411.790.00
avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly129.047.750.00
10,000 get51.3119.495.76e-4
10,000 add & delete randomly190.445.250.00
10,000 addMany137.947.250.00
binary-tree-overall
test nametime taken (ms)executions per secsample deviation
10,000 RBTree add8.13122.971.87e-4
10,000 RBTree add & delete randomly58.3017.155.24e-4
10,000 RBTree get18.1655.062.06e-4
10,000 AVLTree add129.107.750.00
10,000 AVLTree get51.3719.477.26e-4
10,000 AVLTree add & delete randomly189.845.278.96e-4
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.109882.731.12e-6
1,000 addEdge6.18161.767.54e-4
1,000 getVertex0.052.17e+43.35e-7
1,000 getEdge23.3642.810.00
tarjan208.844.790.01
topologicalSort178.785.590.00
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push217.734.590.05
1,000,000 unshift200.414.990.04
1,000,000 unshift & shift168.755.930.04
1,000,000 addBefore298.213.350.06
singly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push & shift196.655.090.04
10,000 push & pop221.634.510.02
10,000 addBefore249.404.010.01
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add28.9534.550.00
100,000 add & poll76.2513.116.57e-4
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push39.7225.180.01
1,000,000 push & pop49.1820.330.01
[//]: # (No deletion!!! End of Replace Section) ## supported module system Now you can use it in Node.js and browser environments CommonJS:**`require export.modules =`** ESModule:   **`import export`** Typescript:   **`import export`** UMD:           **`var Deque = dataStructureTyped.Deque`** ### CDN Copy the line below into the head tag in an HTML document. #### development ```html ``` #### production ```html ``` Copy the code below into the script tag of your HTML, and you're good to go with your development. ```js const { Heap } = dataStructureTyped; const { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultiMap, DirectedVertex, AVLTreeNode } = dataStructureTyped; ```