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
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
L2
compute L2 distance between two vectors
xtod
compute pairwise distance in all vectors in X
d2p
compute (p_{i|j} + p_{j|i})/(2n)
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