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

@jwalsh/tsnejs

v0.1.3

Published

t-SNE visualization algorithm

Downloads

15

Readme

tSNEJS

tSNEJS is an implementation of t-SNE visualization algorithm in Javascript.

t-SNE is a visualization algorithm that embeds things in 2 or 3 dimensions. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify clusters in your data.

Online demo

The main project website has a live example and more description.

There is also the t-SNE CSV demo that allows you to simply paste CSV data into a textbox and tSNEJS computes and visualizes the embedding on the fly (no coding needed).

Research Paper

The algorithm was originally described in this paper:

L.J.P. van der Maaten and G.E. Hinton.
Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research
9(Nov):2579-2605, 2008.

You can find the PDF here.

Example

npm --save i @jwalsh/tsnejs
import * as tsnejs from '@jwalsh/tsnejs';

const opt = {
  epsilon: 10,    // epsilon is learning rate (10 = default)
  perplexity: 30, // roughly how many neighbors each point influences (30 = default)
  dim: 2 // dimensionality of the embedding (2 = default)
};

const tsne = new tsnejs.tSNE(opt); // create a tSNE instance

// initialize data. Here we have 3 points and some example pairwise dissimilarities
const dists = [[1.0, 0.1, 0.2], [0.1, 1.0, 0.3], [0.2, 0.1, 1.0]];
tsne.initDataDist(dists);

// every time you call this, solution gets better
[...Array(500)].forEach((_, i) => tsne.step());

const Y = tsne.getSolution(); // Y is an array of 2-D points that you can plot

The data can be passed to tSNEJS as a set of high-dimensional points using the tsne.initDataRaw(X) function, where X is an array of arrays (high-dimensional points that need to be embedded). The algorithm computes the Gaussian kernel over these points and then finds the appropriate embedding.

API

getopt

syntax sugar

Parameters

  • opt
  • field
  • defaultval

return_v

return 0 mean unit standard deviation random number

randn

return random normal number

Parameters

  • mu
  • std

zeros

utilitity that creates contiguous vector of zeros of size n

Parameters

  • n

randn2d

utility that returns 2d array filled with random numbers or with value s, if provided

Parameters

  • n
  • d
  • s

L2

compute L2 distance between two vectors

Parameters

  • x1
  • x2

xtod

compute pairwise distance in all vectors in X

Parameters

  • X

d2p

compute (p_{i|j} + p_{j|i})/(2n)

Parameters

  • D
  • perplexity
  • tol

sign

helper function

Parameters

  • x

tSNE

t-SNE visualization algorithm

Web Demos

There are two web interfaces to this library that we are aware of:

  • By Andrej, here.
  • By Laurens, here, which takes data in different format and can also use Google Spreadsheet input.

About

Send questions to @karpathy.

License

MIT