stack-typed
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1.48.9 • Public • Published

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What

Brief

This is a standalone Stack data structure from the data-structure-typed collection. If you wish to access more data structures or advanced features, you can transition to directly installing the complete data-structure-typed package

How

install

npm

npm i stack-typed --save

yarn

yarn add stack-typed

methods

snippet

TS

JS

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure Unit Test Performance Test API Docs
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
Heap Heap
Priority Queue PriorityQueue
Max Priority Queue MaxPriorityQueue
Min Priority Queue MinPriorityQueue
Trie Trie
Graph AbstractGraph
Directed Graph DirectedGraph
Undirected Graph UndirectedGraph
Queue Queue
Deque Deque
Linked List SinglyLinkedList
Singly Linked List SinglyLinkedList
Doubly Linked List DoublyLinkedList
Stack Stack

Standard library data structure comparison

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> - -
- 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> -
Set<E> - HashSet<E> set
Map<K, V> - HashMap<K, V> dict
- unordered_set<T> HashSet<E> -
Map<K, V> - - OrderedDict
- unordered_multiset - Counter
- - LinkedHashSet<E> -
HashMap<K, V> - LinkedHashMap<K, V> -
- unordered_multimap<K, V> - -
- bitset<N> - -

Benchmark

avl-tree
test name time taken (ms) executions per sec sample deviation
10,000 add randomly 31.32 31.93 3.67e-4
10,000 add & delete randomly 70.90 14.10 0.00
10,000 addMany 40.58 24.64 4.87e-4
10,000 get 27.31 36.62 2.00e-4
binary-tree
test name time taken (ms) executions per sec sample deviation
1,000 add randomly 12.35 80.99 7.17e-5
1,000 add & delete randomly 15.98 62.58 7.98e-4
1,000 addMany 10.96 91.27 0.00
1,000 get 18.61 53.73 0.00
1,000 dfs 164.20 6.09 0.04
1,000 bfs 58.84 17.00 0.01
1,000 morris 256.66 3.90 7.70e-4
bst
test name time taken (ms) executions per sec sample deviation
10,000 add randomly 31.59 31.66 2.74e-4
10,000 add & delete randomly 74.56 13.41 8.32e-4
10,000 addMany 29.16 34.30 0.00
10,000 get 29.24 34.21 0.00
rb-tree
test name time taken (ms) executions per sec sample deviation
100,000 add 85.85 11.65 0.00
100,000 add & delete randomly 211.54 4.73 0.00
100,000 getNode 37.92 26.37 1.65e-4
comparison
test name time taken (ms) executions per sec sample deviation
SRC PQ 10,000 add 0.57 1748.73 4.96e-6
CJS PQ 10,000 add 0.57 1746.69 4.91e-6
MJS PQ 10,000 add 0.57 1749.68 4.43e-6
SRC PQ 10,000 add & pop 3.47 288.14 6.38e-4
CJS PQ 10,000 add & pop 3.39 295.36 3.90e-5
MJS PQ 10,000 add & pop 3.37 297.17 3.03e-5
directed-graph
test name time taken (ms) executions per sec sample deviation
1,000 addVertex 0.10 9534.93 8.72e-7
1,000 addEdge 6.30 158.67 0.00
1,000 getVertex 0.05 2.16e+4 3.03e-7
1,000 getEdge 22.31 44.82 0.00
tarjan 210.90 4.74 0.01
tarjan all 214.72 4.66 0.01
topologicalSort 172.52 5.80 0.00
hash-map
test name time taken (ms) executions per sec sample deviation
1,000,000 set 275.88 3.62 0.12
1,000,000 Map set 211.66 4.72 0.01
1,000,000 Set add 177.72 5.63 0.02
1,000,000 set & get 317.60 3.15 0.02
1,000,000 Map set & get 274.99 3.64 0.03
1,000,000 Set add & has 172.23 5.81 0.02
1,000,000 ObjKey set & get 929.40 1.08 0.07
1,000,000 Map ObjKey set & get 310.02 3.23 0.05
1,000,000 Set ObjKey add & has 283.28 3.53 0.04
heap
test name time taken (ms) executions per sec sample deviation
10,000 add & pop 5.80 172.35 8.78e-5
10,000 fib add & pop 357.92 2.79 0.00
doubly-linked-list
test name time taken (ms) executions per sec sample deviation
1,000,000 push 221.57 4.51 0.03
1,000,000 unshift 229.02 4.37 0.07
1,000,000 unshift & shift 169.21 5.91 0.02
1,000,000 insertBefore 314.48 3.18 0.07
singly-linked-list
test name time taken (ms) executions per sec sample deviation
10,000 push & pop 212.98 4.70 0.01
10,000 insertBefore 250.68 3.99 0.01
max-priority-queue
test name time taken (ms) executions per sec sample deviation
10,000 refill & poll 8.91 112.29 2.26e-4
priority-queue
test name time taken (ms) executions per sec sample deviation
100,000 add & pop 103.59 9.65 0.00
deque
test name time taken (ms) executions per sec sample deviation
1,000,000 push 14.55 68.72 6.91e-4
1,000,000 push & pop 23.40 42.73 5.94e-4
1,000,000 push & shift 24.41 40.97 1.45e-4
1,000,000 unshift & shift 22.56 44.32 1.30e-4
queue
test name time taken (ms) executions per sec sample deviation
1,000,000 push 39.90 25.07 0.01
1,000,000 push & shift 81.79 12.23 0.00
stack
test name time taken (ms) executions per sec sample deviation
1,000,000 push 37.60 26.60 0.00
1,000,000 push & pop 47.01 21.27 0.00
trie
test name time taken (ms) executions per sec sample deviation
100,000 push 45.97 21.76 0.00
100,000 getWords 66.20 15.11 0.00

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

Software Engineering Design Standards

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.

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Install

npm i stack-typed

Weekly Downloads

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Version

1.48.9

License

MIT

Unpacked Size

1.74 MB

Total Files

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Last publish

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