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# tSNEJS | ||
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tSNEJS is an implementation of t-SNE visualization algorithm in Javascript. | ||
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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. | ||
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## Online demo | ||
The main project website has a [live example](https://cs.stanford.edu/people/karpathy/tsnejs/) and more description. | ||
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## Research Paper | ||
The algorithm was originally described in this paper: | ||
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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. | ||
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You can find the PDF [here](https://jmlr.csail.mit.edu/papers/volume9/vandermaaten08a/vandermaaten08a.pdf). | ||
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## Example code | ||
Import tsne.js into your document: `<script src="tsne.js"></script>` | ||
And then here is some example code: | ||
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var opt = {epsilon: 10}; // epsilon is learning rate (10 = default) | ||
var tsne = new tsnejs.tSNE(opt); // create a tSNE instance | ||
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// 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); | ||
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for(var k = 0; k < 500; k++) { | ||
tsne.step(); // every time you call this, solution gets better | ||
} | ||
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var Y = tsne.getSolution(); // Y is an array of 2-D points that you can plot | ||
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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. | ||
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## About | ||
Send questions to [@karpathy](https://twitter.com/karpathy). |
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