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
Data Structures
| Data Structure | Unit Test | Performance Test | API Documentation | Implemented |
|---|---|---|---|---|
| Binary Tree | 
|
|
Binary Tree | |
| Binary Search Tree (BST) | |
|
BST | |
| AVL Tree | |
|
AVLTree | |
| Tree Multiset | |
|
TreeMultiset | |
| Segment Tree | SegmentTree | |
||
| Binary Indexed Tree | |
BinaryIndexedTree | |
|
| Graph | |
|
AbstractGraph | |
| Directed Graph | |
|
DirectedGraph | |
| Undirected Graph | |
|
UndirectedGraph | |
| Linked List | |
|
SinglyLinkedList | |
| Singly Linked List | |
|
SinglyLinkedList | |
| Doubly Linked List | |
|
DoublyLinkedList | |
| Queue | |
|
Queue | |
| Object Deque | |
|
ObjectDeque | |
| Array Deque | |
|
ArrayDeque | |
| Stack | |
Stack | |
|
| Coordinate Set | CoordinateSet | |
||
| Coordinate Map | CoordinateMap | |
||
| Heap | |
|
Heap | |
| Priority Queue | |
|
PriorityQueue | |
| Max Priority Queue | |
|
MaxPriorityQueue | |
| Min Priority Queue | |
|
MinPriorityQueue | |
| Trie | |
Trie | |
Why
Complexities
performance of Big O
| Big O Notation | Type | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
|---|---|---|---|---|
| O(1) | Constant | 1 | 1 | 1 |
| O(log N) | Logarithmic | 3 | 6 | 9 |
| O(N) | Linear | 10 | 100 | 1000 |
| O(N log N) | n log(n) | 30 | 600 | 9000 |
| O(N^2) | Quadratic | 100 | 10000 | 1000000 |
| O(2^N) | Exponential | 1024 | 1.26e+29 | 1.07e+301 |
| O(N!) | Factorial | 3628800 | 9.3e+157 | 4.02e+2567 |
Data Structure Complexity
| Data Structure | Access | Search | Insertion | Deletion | Comments |
|---|---|---|---|---|---|
| Array | 1 | n | n | n | |
| Stack | n | n | 1 | 1 | |
| Queue | n | n | 1 | 1 | |
| Linked List | n | n | 1 | n | |
| Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) |
| Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) |
| B-Tree | log(n) | log(n) | log(n) | log(n) | |
| Red-Black Tree | log(n) | log(n) | log(n) | log(n) | |
| AVL Tree | log(n) | log(n) | log(n) | log(n) | |
| Bloom Filter | - | 1 | 1 | - | False positives are possible while searching |
Sorting Complexity
| Name | Best | Average | Worst | Memory | Stable | Comments |
|---|---|---|---|---|---|---|
| Bubble sort | n | n2 | n2 | 1 | Yes | |
| Insertion sort | n | n2 | n2 | 1 | Yes | |
| Selection sort | n2 | n2 | n2 | 1 | No | |
| Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | |
| Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | |
| Quick sort | n log(n) | n log(n) | n2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space |
| Shell sort | n log(n) | depends on gap sequence | n (log(n))2 | 1 | No | |
| Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array |
| Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key |
