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

heap sort TS

    import {Heap, MaxHeap, MinHeap} from 'data-structure-typed';

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

// Use Heap to sort an array
function heapSort(arr: number[]): number[] {
    const heap = new Heap<number>(arr, {comparator: (a, b) => a - b});
    const sorted: number[] = [];
    while (!heap.isEmpty()) {
        sorted.push(heap.poll()!); // Poll minimum element
    }
    return sorted;
}

console.log('Heap sorted:', heapSort([5, 3, 8, 4, 1, 2]));  // [1, 2, 3, 4, 5, 8];

top K problem TS

// Use Heap to resolve top K problem
function topKElements(arr: number[], k: number): number[] {
    const heap = new Heap<number>([], {comparator: (a, b) => b - a}); // Max heap
    arr.forEach((num) => {
        heap.add(num);
        if (heap.size > k) heap.poll(); // Keep the heap size at K
    });
    return heap.toArray();
}

const numbers = [10, 30, 20, 5, 15, 25];
console.log('Top K:', topKElements(numbers, 3)); // [15, 10, 5]

real-time median TS

// Use Heap to maintain median value for real-time retrieval
class MedianFinder {
    private low: MaxHeap<number>; // Max heap, stores the smaller half
    private high: MinHeap<number>; // Min heap, stores the larger half

    constructor() {
        this.low = new MaxHeap<number>([]);
        this.high = new MinHeap<number>([]);
    }

    addNum(num: number): void {
        if (this.low.isEmpty() || num <= this.low.peek()!) this.low.add(num);
        else this.high.add(num);

        // Balance heaps
        if (this.low.size > this.high.size + 1) this.high.add(this.low.poll()!);
        else if (this.high.size > this.low.size) this.low.add(this.high.poll()!);
    }

    findMedian(): number {
        return this.low.peek()!;
    }
}

const medianFinder = new MedianFinder();
medianFinder.addNum(10);
console.log('realtime median: ', medianFinder.findMedian()) // 10
medianFinder.addNum(20);
console.log('realtime median: ', medianFinder.findMedian()) // 10
medianFinder.addNum(30);
console.log('realtime median: ', medianFinder.findMedian()) // 20
medianFinder.addNum(40);
console.log('realtime median: ', medianFinder.findMedian()) // 20
medianFinder.addNum(50);
console.log('realtime median: ', medianFinder.findMedian()) // 30

load balance TS

// Use Heap for load balancing
function loadBalance(requests: number[], servers: number): number[] {
    const serverHeap = new Heap<{ id: number; load: number }>([], { comparator: (a, b) => a.load - b.load }); // min heap
    const serverLoads = new Array(servers).fill(0);

    for (let i = 0; i < servers; i++) {
        serverHeap.add({ id: i, load: 0 });
    }

    requests.forEach(req => {
        const server = serverHeap.poll()!;
        serverLoads[server.id] += req;
        server.load += req;
        serverHeap.add(server); // The server after updating the load is re-entered into the heap
    });

    return serverLoads;
}

const requests = [5, 2, 8, 3, 7];
const serversLoads = loadBalance(requests, 3);
console.log('server loads: ', serversLoads);  // [12, 8, 5]

conventional operation TS

const minNumHeap = new MinHeap<number>([1, 6, 2, 0, 5]);
minNumHeap.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 }>([], {comparator: (a, b) => b.keyA - a.keyA});
const obj1 = {keyA: 'a1'}, obj6 = {keyA: 'a6'}, obj5 = {keyA: 'a5'}, obj2 = {keyA: 'a2'},
    obj0 = {keyA: 'a0'}, obj9 = {keyA: 'a9'};

maxHeap.add(obj1);
maxHeap.has(obj1)                       // true
maxHeap.has(obj9)                       // false
maxHeap.add(obj6);
maxHeap.has(obj6)                       // true
maxHeap.add(obj5);
maxHeap.add(obj2);
maxHeap.add(obj0);
maxHeap.add(obj9);
maxHeap.has(obj9)                       // true

const peek9 = maxHeap.peek();
console.log(peek9.keyA)                 // 'a9'

const heapToArr = maxHeap.toArray();
console.log(heapToArr.map(ele => ele?.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();
    console.log(polled.keyA)           // values[i]
    i++;
}

conventional operation JS

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

const minNumHeap = new MinHeap([1, 6, 2, 0, 5]);
minNumHeap.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([], {comparator: (a, b) => b.keyA - a.keyA});
const obj1 = {keyA: 'a1'}, obj6 = {keyA: 'a6'}, obj5 = {keyA: 'a5'}, obj2 = {keyA: 'a2'},
    obj0 = {keyA: 'a0'}, obj9 = {keyA: 'a9'};

maxHeap.add(obj1);
maxHeap.has(obj1)                       // true
maxHeap.has(obj9)                       // false
maxHeap.add(obj6);
maxHeap.has(obj6)                       // true
maxHeap.add(obj5);
maxHeap.add(obj2);
maxHeap.add(obj0);
maxHeap.add(obj9);
maxHeap.has(obj9)                       // true

const peek9 = maxHeap.peek();
console.log(peek9.keyA)             // 'a9'

const heapToArr = maxHeap.toArray();
console.log(heapToArr.map(ele => ele?.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();
    console.log(polled.keyA)           // values[i]
    i++;
}

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Heap			Heap

Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
Heap<E>	priority_queue<T>	PriorityQueue<E>	heapq

Benchmark

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

Built-in classic algorithms

Algorithm	Function Description	Iteration Type

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.