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https://github.com/chrvadala/sliding-window-max

Given a stream of data, this algorithm returns (for every added value) the current max value.
https://github.com/chrvadala/sliding-window-max

algorithm javascript

Last synced: about 1 month ago
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Given a stream of data, this algorithm returns (for every added value) the current max value.

Host: GitHub
URL: https://github.com/chrvadala/sliding-window-max
Owner: chrvadala
License: mit
Created: 2018-11-10T12:34:33.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2020-06-01T11:21:04.000Z (over 4 years ago)
Last Synced: 2024-10-05T12:06:53.766Z (about 1 month ago)
Topics: algorithm, javascript
Language: JavaScript
Homepage:
Size: 728 KB
Stars: 1
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        # sliding-window-max

[![chrvadala](https://img.shields.io/badge/website-chrvadala-orange.svg)](https://chrvadala.github.io)

[![Test](https://github.com/chrvadala/sliding-window-max/workflows/Test/badge.svg)](https://github.com/chrvadala/sliding-window-max/actions)

[![Coverage Status](https://coveralls.io/repos/github/chrvadala/sliding-window-max/badge.svg?branch=master)](https://coveralls.io/github/chrvadala/sliding-window-max?branch=master)

[![npm](https://img.shields.io/npm/v/sliding-window-max.svg?maxAge=2592000?style=plastic)](https://www.npmjs.com/package/sliding-window-max)

[![Downloads](https://img.shields.io/npm/dm/sliding-window-max.svg)](https://www.npmjs.com/package/sliding-window-max)

[![Donate](https://img.shields.io/badge/donate-PayPal-green.svg)](https://www.paypal.me/chrvadala/25)

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

```javascript

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:

 - evaluate the data as soon as they are produced

 - evaluate big array using less memory

## Changelog

- **0.x** - Beta version

- **1.0** - First official version

- **1.1** - Migrates to gh-workflows; Upgrades deps

 ## Contributors

 - [chrvadala](https://github.com/chrvadala) (author)