npm package: @jwalsh/tsnejs
Description: t-SNE visualization algorithm
Latest version: 0.1.3

# 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](http://cs.stanford.edu/people/karpathy/tsnejs/) and more
description.

There is also the
[t-SNE CSV demo](http://cs.stanford.edu/people/karpathy/tsnejs/csvdemo.html)
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](http://jmlr.csail.mit.edu/papers/volume9/vandermaaten08a/vandermaaten08a.pdf).

## Example

```shell
npm --save i @jwalsh/tsnejs
```

```javascript
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

<!-- Generated by documentation.js. Update this documentation by updating the source code. -->

### 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](http://cs.stanford.edu/people/karpathy/tsnejs/csvdemo.html).
-   By Laurens, [here](http://homepage.tudelft.nl/19j49/tsnejs/), which
    takes data in different format and can also use Google Spreadsheet
    input.

## About

Send questions to [@karpathy](https://twitter.com/karpathy).

## License

MIT