Sliding-window-max NPM

sliding-window-max

Given a stream of data, this algorithm returns (for every added value) the current max value.

It uses a strategy that:

Stores at most a number of values defined by the window size
Performs a rolling max calculation

Example

const SlidingWindowMax = require('sliding-window-max')

const windowSize = 3

const slidingWindowMax = new SlidingWindowMax(windowSize)
slidingWindowMax.add(0) // returns null
slidingWindowMax.add(5) // returns null
slidingWindowMax.add(10)// returns 10
slidingWindowMax.add(7) // returns 10
slidingWindowMax.add(8) // returns 10
slidingWindowMax.add(5) // returns 8
slidingWindowMax.add(3) // returns 8
slidingWindowMax.add(4) // returns 5
//etc ...

VALUE	Max
0 5 10 7 8 5 3 4	`null`
0 5 10 7 8 5 3 4	`null`
0 5 10 7 8 5 3 4	10
0 5 10 7 8 5 3 4	10
0 5 10 7 8 5 3 4	10
0 5 10 7 8 5 3 4	8
0 5 10 7 8 5 3 4	8
0 5 10 7 8 5 3 4	5
...	...

Reference

`new SlidingWindowMax(windowSize, options)`

Param	Default	Description
windowSize	required	Defines how many values should be considered to calculate the max
options.comparator	`(a, b) => b - a`	Override the custom comparator function
options.waitFullRange	true	If false the functions returns the max value also if the window size hasn't been reached yet

`add(value)`

Evaluate a new value and returns the calculated max value

Credits

I made this algorithm starting from this code https://github.com/chihungyu1116/leetcode-javascript/blob/master/239%20Sliding%20Window%20Maximum.js. My requirements were a bit different. The leetcode algorithm requires that all the data are known before it starts. With sliding-window-max you can: