test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 add randomly | 72.48 | 13.80 | 0.03 |
10,000 add & delete randomly | 144.14 | 6.94 | 0.03 |
10,000 addMany | 69.71 | 14.35 | 0.02 |
10,000 get | 54.21 | 18.45 | 0.01 |
# 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))
JavaScript和TypeScript的数据结构。 是否羡慕C++ [STL]() (std::)、Python的 [collections]() 和Java的 [java.util]()? 不再需要羡慕了!JavaScript和TypeScript现在拥有 [data-structure-typed]()。 **`基准测试`** 与C++ STL相比。**`API 标准`** 与ES6和Java对齐。**`易用性`** 可与Python媲美。 [//]: # (![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)) ## 安装和使用 现在你可以在 Node.js 和浏览器环境中使用它 CommonJS:**`require export.modules =`** ESModule: **`import export`** Typescript: **`import export`** UMD: **`var Deque = dataStructureTyped.Deque`** ### npm ```bash npm i data-structure-typed --save ``` ### 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 将下面的代码复制到 HTML 文档的头标签中。 #### 开发环境 ```html ``` #### 生产环境 ```html ``` 将下面的代码复制到你的 HTML 的 script 标签中,你就可以开始你的开发了。 ```js const {Heap} = dataStructureTyped; const { BinaryTree, Graph, Queue, Stack, PriorityQueue, BST, Trie, DoublyLinkedList, AVLTree, MinHeap, SinglyLinkedList, DirectedGraph, TreeMultimap, DirectedVertex, AVLTreeNode } = dataStructureTyped; ``` ## 生动示例 ### Binary Tree(二叉树) [试一下](https://vivid-algorithm.vercel.app/),或者你可以使用我们的可视化工具运行自己的代码 [visual tool](https://github.com/zrwusa/vivid-algorithm) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-array-to-binary-tree.webp) ### Binary Tree DFS (二叉搜索树深度遍历) [试一下](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/binary-tree-dfs-in-order.webp) ### AVL Tree(AVL树) [试一下](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/avl-tree-test.webp) ### Tree Multi Map [试一下](https://vivid-algorithm.vercel.app/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/tree-multiset-test.webp) ### Matrix [试一下](https://vivid-algorithm.vercel.app/algorithm/graph/) ![](https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/examples/videos/webp_output/matrix-cut-off-tree-for-golf.webp) ### 有向图 [试一下](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) ### 地图 [试一下](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) ## 代码片段 ### 二叉搜索树 (BST) 代码示例 #### TS ```ts import {BST, BSTNode} from 'data-structure-typed'; const bst = new BSTData 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 |
Data Structure Typed | C++ STL | java.util | Python collections |
---|---|---|---|
Heap<E> | priority_queue<T> | PriorityQueue<E> | heapq |
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> | - | - |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 add randomly | 72.48 | 13.80 | 0.03 |
10,000 add & delete randomly | 144.14 | 6.94 | 0.03 |
10,000 addMany | 69.71 | 14.35 | 0.02 |
10,000 get | 54.21 | 18.45 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000 add randomly | 15.84 | 63.14 | 0.00 |
1,000 add & delete randomly | 24.62 | 40.62 | 0.00 |
1,000 addMany | 17.85 | 56.01 | 0.00 |
1,000 get | 20.83 | 48.00 | 0.00 |
1,000 has | 20.78 | 48.13 | 0.00 |
1,000 dfs | 186.06 | 5.37 | 0.02 |
1,000 bfs | 66.58 | 15.02 | 0.02 |
1,000 morris | 298.23 | 3.35 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 add randomly | 55.04 | 18.17 | 0.01 |
10,000 add & delete randomly | 129.85 | 7.70 | 0.01 |
10,000 addMany | 50.40 | 19.84 | 0.01 |
10,000 get | 63.39 | 15.78 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add | 113.25 | 8.83 | 0.02 |
100,000 add & delete randomly | 305.28 | 3.28 | 0.03 |
100,000 getNode | 73.20 | 13.66 | 0.03 |
100,000 add & iterator | 159.80 | 6.26 | 0.06 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
SRC PQ 10,000 add | 0.17 | 5872.02 | 4.08e-5 |
CJS PQ 10,000 add | 0.20 | 4961.22 | 1.14e-4 |
MJS PQ 10,000 add | 0.74 | 1351.47 | 2.98e-4 |
SRC PQ 10,000 add & pop | 4.62 | 216.49 | 0.00 |
CJS PQ 10,000 add & pop | 4.36 | 229.40 | 0.00 |
MJS PQ 10,000 add & pop | 3.92 | 255.23 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000 addVertex | 0.12 | 8557.70 | 2.46e-5 |
1,000 addEdge | 7.37 | 135.70 | 0.00 |
1,000 getVertex | 0.05 | 1.91e+4 | 1.12e-5 |
1,000 getEdge | 22.75 | 43.96 | 0.00 |
tarjan | 196.98 | 5.08 | 0.01 |
tarjan all | 217.25 | 4.60 | 0.03 |
topologicalSort | 177.30 | 5.64 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 set | 153.74 | 6.50 | 0.07 |
1,000,000 Map set | 330.02 | 3.03 | 0.16 |
1,000,000 Set add | 258.64 | 3.87 | 0.06 |
1,000,000 set & get | 138.80 | 7.20 | 0.06 |
1,000,000 Map set & get | 352.63 | 2.84 | 0.05 |
1,000,000 Set add & has | 217.97 | 4.59 | 0.02 |
1,000,000 ObjKey set & get | 414.87 | 2.41 | 0.06 |
1,000,000 Map ObjKey set & get | 389.17 | 2.57 | 0.07 |
1,000,000 Set ObjKey add & has | 352.67 | 2.84 | 0.03 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add & pop | 90.67 | 11.03 | 0.02 |
100,000 add & dfs | 40.30 | 24.81 | 0.01 |
10,000 fib add & pop | 414.94 | 2.41 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 290.62 | 3.44 | 0.10 |
1,000,000 unshift | 253.88 | 3.94 | 0.10 |
1,000,000 unshift & shift | 259.65 | 3.85 | 0.14 |
1,000,000 insertBefore | 463.16 | 2.16 | 0.10 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push & shift | 250.27 | 4.00 | 0.08 |
10,000 push & pop | 261.13 | 3.83 | 0.03 |
10,000 insertBefore | 282.46 | 3.54 | 0.02 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
10,000 refill & poll | 10.49 | 95.29 | 0.00 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 add & pop | 110.63 | 9.04 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 15.89 | 62.92 | 0.00 |
1,000,000 push & pop | 26.45 | 37.81 | 0.01 |
1,000,000 push & shift | 27.52 | 36.34 | 0.00 |
1,000,000 unshift & shift | 28.82 | 34.70 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 51.21 | 19.53 | 0.02 |
1,000,000 push & shift | 105.56 | 9.47 | 0.05 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
1,000,000 push | 43.57 | 22.95 | 0.01 |
1,000,000 push & pop | 55.18 | 18.12 | 0.01 |
test name | time taken (ms) | executions per sec | sample deviation |
---|---|---|---|
100,000 push | 54.08 | 18.49 | 0.01 |
100,000 getWords | 77.77 | 12.86 | 0.02 |
算法 | 功能描述 | 迭代类型 |
---|---|---|
二叉树深度优先搜索(DFS) | 以深度优先的方式遍历二叉树,从根节点开始,首先访问左子树,然后是右子树,使用递归。 | 递归 + 迭代 |
二叉树广度优先搜索(BFS) | 以广度优先的方式遍历二叉树,从根节点开始,逐层从左到右访问节点。 | 迭代 |
图的深度优先搜索 | 以深度优先的方式遍历图,从给定节点开始,尽可能深地沿一条路径探索,然后回溯以探索其他路径。用于寻找连通分量、路径等。 | 递归 + 迭代 |
二叉树Morris遍历 | Morris遍历是一种中序遍历二叉树的算法,空间复杂度为O(1)。它允许在没有额外栈或递归的情况下遍历树。 | 迭代 |
图的广度优先搜索 | 以广度优先的方式遍历图,从给定节点开始,首先访问与起始节点直接相连的节点,然后逐层扩展。用于寻找最短路径等。 | 递归 + 迭代 |
图的Tarjan算法 | 在图中找到强连通分量,通常使用深度优先搜索实现。 | 递归 |
图的Bellman-Ford算法 | 从单一源点找到最短路径,可以处理负权边 | 迭代 |
图的Dijkstra算法 | 从单一源点找到最短路径,不能处理负权边 | 迭代 |
图的Floyd-Warshall算法 | 找到所有节点对之间的最短路径 | 迭代 |
图的getCycles | 在图中找到所有循环或检测循环的存在。 | 递归 |
图的getCutVertexes | 在图中找到切点,这些是移除后会增加图中连通分量数量的节点。 | 递归 |
图的getSCCs | 在图中找到强连通分量,这些是任意两个节点都可以相互到达的子图。 | 递归 |
图的getBridges | 在图中找到桥,这些是移除后会增加图中连通分量数量的边。 | 递归 |
图的拓扑排序 | 对有向无环图(DAG)进行拓扑排序,以找到节点的线性顺序,使得所有有向边都从较早的节点指向较晚的节点。 | 递归 |
原则 | 描述 |
---|---|
实用性 | 遵循ES6和ESNext标准,提供统一且考虑周到的可选参数,简化方法名称。 |
可扩展性 | 遵循OOP(面向对象编程)原则,允许所有数据结构继承。 |
模块化 | 包括数据结构模块化和独立的NPM包。 |
效率 | 所有方法都提供时间和空间复杂度,可与原生JS性能相媲美。 |
可维护性 | 遵循开源社区开发标准,完整文档,持续集成,并遵循TDD(测试驱动开发)模式。 |
可测试性 | 自动化和定制单元测试、性能测试和集成测试。 |
可移植性 | 计划移植到Java、Python和C++,目前已完成80%。 |
可复用性 | 完全解耦,最小化副作用,遵循OOP。 |
安全性 | 精心设计的成员变量和方法的安全性。读写分离。数据结构软件不需要考虑其他安全方面。 |
可扩展性 | 数据结构软件不涉及负载问题。 |