heap-typed

What

Brief

This is a standalone Heap 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 heap-typed --save

yarn

yarn add heap-typed

methods

Min Heap Max Heap

snippet

TS

    import {MinHeap, MaxHeap} from 'data-structure-typed';
    // /* or if you prefer */ import {MinHeap, MaxHeap} from 'heap-typed';

    const minNumHeap = new MinHeap<number>();
    minNumHeap.add(1).add(6).add(2).add(0).add(5).add(9);
    minNumHeap.has(1)        //  true
    minNumHeap.has(2)        //  true
    minNumHeap.poll()        //  0
    minNumHeap.poll()        //  1
    minNumHeap.peek()        //  2
    minNumHeap.has(1);       // false
    minNumHeap.has(2);       // true
    const arrFromHeap = minNumHeap.toArray();
    arrFromHeap.length       //  4
    arrFromHeap[0]           //  2
    arrFromHeap[1]           //  5
    arrFromHeap[2]           //  9
    arrFromHeap[3]           //  6
    minNumHeap.sort()        //  [2, 5, 6, 9]
    
    
    const maxHeap = new MaxHeap<{ keyA: string }>();
    const myObj1 = {keyA: 'a1'}, myObj6 = {keyA: 'a6'}, myObj5 = {keyA: 'a5'}, myObj2 = {keyA: 'a2'},
        myObj0 = {keyA: 'a0'}, myObj9 = {keyA: 'a9'};
    maxHeap.add(1, myObj1);
    maxHeap.has(myObj1)  // true
    maxHeap.has(myObj9)  // false
    maxHeap.add(6, myObj6);
    maxHeap.has(myObj6)  // true
    maxHeap.add(5, myObj5);
    maxHeap.has(myObj5)  // true
    maxHeap.add(2, myObj2);
    maxHeap.has(myObj2)  // true
    maxHeap.has(myObj6)  // true
    maxHeap.add(0, myObj0);
    maxHeap.has(myObj0)  // true
    maxHeap.has(myObj9)  // false
    maxHeap.add(9, myObj9);
    maxHeap.has(myObj9)  // true
    
    const peek9 = maxHeap.peek(true);
    peek9 && peek9.val && peek9.val.keyA  // 'a9'
    
    const heapToArr = maxHeap.toArray(true);
    heapToArr.map(item => item?.val?.keyA)  // ['a9', 'a2', 'a6', 'a1', 'a0', 'a5']
    
    const values = ['a9', 'a6', 'a5', 'a2', 'a1', 'a0'];
    let i = 0;
    while (maxHeap.size > 0) {
        const polled = maxHeap.poll(true);
        polled && polled.val && polled.val.keyA  // values[i]
        i++;
    }

JS

    const {MinHeap, MaxHeap} = require('data-structure-typed');
    // /* or if you prefer */ const {MinHeap, MaxHeap} = require('heap-typed');

    const minNumHeap = new MinHeap();
    minNumHeap.add(1).add(6).add(2).add(0).add(5).add(9);
    minNumHeap.has(1)        //  true
    minNumHeap.has(2)        //  true
    minNumHeap.poll()        //  0
    minNumHeap.poll()        //  1
    minNumHeap.peek()        //  2
    minNumHeap.has(1);       // false
    minNumHeap.has(2);       // true
    const arrFromHeap = minNumHeap.toArray();
    arrFromHeap.length       //  4
    arrFromHeap[0]           //  2
    arrFromHeap[1]           //  5
    arrFromHeap[2]           //  9
    arrFromHeap[3]           //  6
    minNumHeap.sort()        //  [2, 5, 6, 9]
    
    
    const maxHeap = new MaxHeap();
    const myObj1 = {keyA: 'a1'}, myObj6 = {keyA: 'a6'}, myObj5 = {keyA: 'a5'}, myObj2 = {keyA: 'a2'},
        myObj0 = {keyA: 'a0'}, myObj9 = {keyA: 'a9'};
    maxHeap.add(1, myObj1);
    maxHeap.has(myObj1)  // true
    maxHeap.has(myObj9)  // false
    maxHeap.add(6, myObj6);
    maxHeap.has(myObj6)  // true
    maxHeap.add(5, myObj5);
    maxHeap.has(myObj5)  // true
    maxHeap.add(2, myObj2);
    maxHeap.has(myObj2)  // true
    maxHeap.has(myObj6)  // true
    maxHeap.add(0, myObj0);
    maxHeap.has(myObj0)  // true
    maxHeap.has(myObj9)  // false
    maxHeap.add(9, myObj9);
    maxHeap.has(myObj9)  // true
    
    const peek9 = maxHeap.peek(true);
    peek9 && peek9.val && peek9.val.keyA  // 'a9'
    
    const heapToArr = maxHeap.toArray(true);
    heapToArr.map(item => item?.val?.keyA)  // ['a9', 'a2', 'a6', 'a1', 'a0', 'a5']
    
    const values = ['a9', 'a6', 'a5', 'a2', 'a1', 'a0'];
    let i = 0;
    while (maxHeap.size > 0) {
        const polled = maxHeap.poll(true);
        polled && polled.val && polled.val.keyA  // values[i]
        i++;
    }

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.