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. See example below.

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 code

Import tsne.js into your document: <script src="tsne.js"></script> And then here is some example code:

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

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

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

for(var k = 0; k < 500; k++) {
  tsne.step(); // every time you call this, solution gets better
}

var 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.

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

DaehongKim1/tsnejs