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

look-alike

v1.0.1

Published

A simple-yet-powerful KD-tree library for NodeJS, with support for lightning-fast k-Nearest Neighbour queries. Supports normalization, weights, key and filter parameters

Downloads

115

Readme

Look-Alike

Build Status

Twin brother of Alike. They look quite alike. Except this brother is more heavy-weight and likes playing in trees.

A simple-yet-powerful KD-tree library for NodeJS, with support for lightning-fast k-Nearest Neighbour queries. Supports normalization, weights, key and filter parameters.

Look-Alike vs Alike

But if Look-Alike is more powerful than Alike, why use Alike at all? Well, TANSTAAFL

Alike is more nimble and functional and does not require state. You can call it with thousands of rows and it will still be fast (n log n). It is also better when the rows change often (no need to rebuild the tree).

Look-Alike builds the tree in advance (which is the only way to beat n log n) and holds it in memory. It supports kNN queries that can be as fast as k log n, so is the prefered brother when you have more than a few thousand rows. The tree does not change -- it would need to be rebuild (at n log2 n) before the query when the rows change.

Quickstart

To install and add it to your package.json

$ npm install look-alike --save

Now you can load up the module and use it like so:

LA = require('look-alike');
tree = new LA([rows]);
top3 = tree.query(subject, {k:3});

Given [rows] is an array of gazillion objects with x numerical attributes, top3 now holds an array of the 3 closest objects to subject. The second-easiest Recommender System you will ever see (aside from Alike).

Overview

Look-Alike exports a single class with a constructor and a single instance method.

The constructor expects an array of objects, and optionally an object of options:

  • objects[Array] - The rows from which we build our tree
  • options[Object] (optional) - which may include:
    • attributes[Array] (optional) - An array of strings which list attributes/dimensions to use in the KD-tree. By default, will use all keys of the first object in objects. Specifying which keys to use for the tree is useful if you want to include additional information in the objects (such as an id, or a label, or more).
    • key[Function] (optional) - A key function, which is used to map over objects to get to the attributes. Useful when the attributes are nested in the parent object.

The instance method is called query and expects the following parameters:

  • subject[Object] - The reference point that we want to find the Nearest Neighbors of
  • options[Object] (optional) - which may include:
    • k[Int] (default = 1) - The number of objects to return. The query complexity is k log n, so the higher this number, the longer the algorithm takes (on average).
    • normalize[Bool] (default = true) - When true, will normalize the attributes when calculating distances (recommended if attributes are not on the same scale). Note: turn off for small number of rows to avoid unexpected results! In those cases you should be using Alike anyway!
    • weights[Object] (optional) - Define weights per attribute (e.g. {x:0.3, y:0.7} would weight attribute y at 70% and x at 30%. Defaults to equal weights)
    • filter[Function] (optional) - When provided, only consider objects in the tree that pass the filter function (i.e. return true)

And returns an array of objects in sorted order. You may look at the test-cases if you don't believe me.

Development

Look-Alike is written in Literate CoffeeScript in the coffee/ folder. Browse around on Github for the best annotated source experience!

Unit tests are in the test/ folder. You can run the tests with npm test or if you are developing, you may use make watch-test to watch while you TDD. :)

License

Look-Alike is licensed under the terms of the GNU Lesser General Public License, known as the LGPL.