npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

unsupervised-knn-js

v2.5.1

Published

Algorithm for fetching the k nearest neighbors of an input vector through distance calculations.

Downloads

29

Readme

Unsupervised-KNN-JS

Build Status Code Coverage Version Code Size License

Node.JS package for computing the k nearest neighbors to an input vector using distance calculations. Computations are implemented in Rust for high perfromance and parallelism.

Table of Contents

Features

  • Parallelized distance computations
  • Fast native system processing
  • 14 popular distance functions
  • Out of the box support on Linux, OSX, and Windows
  • Support for Node 8, 10, 12, and 13

Install

$ npm i unsupervised-knn-js

Import

const { knn } = require('unsupervised-knn-js')

Example

> const { knn } = require('unsupervised-knn-js')

> const neighbors = [
  { label: 'some name', vector: [1, 2, 4, 5] },
  { label: 'name 2', vector: [14, 4, 13, 2] },
  { label: 'another name', vector: [4, 4, 4, 5] },
]
> const target = [1, 2, 3, 4]
> const algo = 'euclidean'
> const k = 2

> knn(algo, k, neighbors, target)
[
  { label: 'some name', distance: 1.4142135623730951 },
  { label: 'another name', distance: 3.872983346207417 }
]
> 

Usage

Parameters

The knn function takes 4 parameters:

  1. Algorithm String
    • This is the algorithm which computes distances between the target and all neighbors
    • The current algorithms natively supported are:
        'euclidean'  // L2 Norm Difference
        'cosine'     // Cosine Distance
        'mae'        // Mean-Absolute-Error
        'mse'        // Mean-Squared-Error 
        'manhattan'  // Sum of Absolute Differences
        'ssd'        // Sum of Squared Differences
        'canberra'   // Weighted Manhatten Distance
        'hamming'    // Sum of Binary Differences
        'L3'         // L3 Norm Difference
        'L4'         // L4 Norm Difference
        'L5'         // L5 Norm Difference
        'L10'        // L10 Norm Difference
        'chebyshev'  // L-Infinite Norm Difference
        'pearson'    // Pearson Correlation Distance
  2. K-Value
    • The amount of closest neighbors to the target point to return
    • So if k = 2, the 2 closests neighbors to the target vector will be returned.
  3. Neighbors
    • This is an array of objects where each object represents a neighbor or point
    • Each object should have a label and vector field as such:
      {
        label: 'name or id',
        vector: [1, 3, 4.5, -4]
      }
    • The following is a valid array of neighbors:
      const neighbors = [
        { label: 'some name', vector: [1, 2, 4, 5] },
        { label: 'name 2', vector: [14, 4, 13, 2] },
        { label: 'another name', vector: [4, 4, 4, 5] },
      ]
  4. Target
    • This is the vector for which to find the closest or most similar points to
    • This should be an array of numbers

Return

The function returns an array of objects representing the closest points to the target.

Each object has a label field for identification and a distance field which represents it's difference from the target.

[
  { label: 'some name', distance: 1.4142135623730951 },
  { label: 'another name', distance: 3.872983346207417 }
]

This list is ordered in ascending order based on the distance field in each object.

Distance Comparisons

Here is an example of the same data run against different distance functions

> const { knn } = require('unsupervised-knn-js')
> const neighbors = [
  { label: 'some name', vector: [1, 2, 4, 5] },
  { label: 'another name', vector: [4, 4, 4, 5] },
  { label: 'name 3', vector: [14, 4, 13, 2] },
]
> const target = [1, 2, 3, 4]

> // Euclidean
> knn('euclidean', 3, neighbors, target)
[
  { label: 'some name', distance: 1.4142135623730951 },
  { label: 'another name', distance: 3.872983346207417 },
  { label: 'name 3', distance: 16.64331697709324 }
]

> // Cosine
> knn('cosine', 3, neighbors, target)
[
  { label: 'some name', distance: 0.003993481192393733 },
  { label: 'another name', distance: 0.059777545024485734 },
  { label: 'name 3', distance: 0.35796589482505503 }
]

> // Mean-Absolute-Error 
> knn('mae', 3, neighbors, target)
[
  { label: 'some name', distance: 0.5 },
  { label: 'another name', distance: 1.75 },
  { label: 'name 2', distance: 6.75 }
]

> // Mean-Squared-Error
> knn('mse', 3, neighbors, target)
[
  { label: 'some name', distance: 0.5 },
  { label: 'another name', distance: 3.75 },
  { label: 'name 3', distance: 69.25 }
]

> // Manhattan
> knn('manhattan', 3, neighbors, target)
[
  { label: 'some name', distance: 2 },
  { label: 'another name', distance: 7 },
  { label: 'name 3', distance: 27 }
]

> // Sum of Squared Differences
> knn('ssd', 3, neighbors, target)
[
  { label: 'some name', distance: 2 },
  { label: 'another name', distance: 15 },
  { label: 'name 2', distance: 277 }
]

> // Canberra
> knn('canberra', 3, neighbors, target)
[
  { label: 'some name', distance: 0.25396825396825395 },
  { label: 'another name', distance: 1.1873015873015873 },
  { label: 'name 3', distance: 2.158333333333333 }
]

> // Hamming
> knn('hamming', 3, neighbors, target)
[
  { label: 'some name', distance: 2 },
  { label: 'another name', distance: 4 },
  { label: 'name 3', distance: 4 }
]

> // L3 Norm Difference
> knn('L3', 3, neighbors, target)
[
  { label: 'some name', distance: 1.2599210498948732 },
  { label: 'another name', distance: 3.332221851645953 },
  { label: 'name 3', distance: 14.756054203376182 }
]

> // L4 Norm Difference
> knn('L4', 3, neighbors, target)
[
  { label: 'some name', distance: 1.189207115002721 },
  { label: 'another name', distance: 3.1543421455299043 },
  { label: 'name 3', distance: 14.016098305349052 }
]

> // L5 Norm Difference
> knn('L5', 3, neighbors, target)
[
  { label: 'some name', distance: 1.148698354997035 },
  { label: 'another name', distance: 3.0796116495812957 },
  { label: 'name 3', distance: 13.635466232760923 }
]

> // L10 Norm Difference
> knn('L10', 3, neighbors, target)
[
  { label: 'some name', distance: 1.0717734625362931 },
  { label: 'another name', distance: 3.0051723058500506 },
  { label: 'name 2', distance: 13.091355843137347 }
]

> // Chebyshev
> knn('chebyshev', 3, neighbors, target)
[
  { label: 'some name', distance: 1 },
  { label: 'another name', distance: 3 },
  { label: 'name 3', distance: 13 }
]

> // Pearson Correlation Distance
> knn('pearson', 3, neighbors, target)
[
  { label: 'some name', distance: 0.010050506338833642 },
  { label: 'another name', distance: 0.2254033307585166 },
  { label: 'name 3', distance: 1.5685785754425927 }
]

Future Features

  • Even more native distance functions
  • Potential implemention of custom distance functions passed in by the user

Ideas and suggestions are welcome!

Changes

For changes please see the Changelog