Visualization, Prototypes, ML, Data wrangling and Art.
tf.tensor([1, 2, 3, 4])
{
const tfdata = tf.randomUniform([2000,10]);
const tsneOpt = tf.tsne.tsne(tfdata);
var a = tsneOpt.compute()
a.then(d => console.log(d)).catch(d => console.log(d))
}
// help! not sure why the graph is not rendered automatically. when running manually it works.
//DOM.download(serialize(data, grid), "gid.csv", "Download grid positions")
//DOM.download(serialize(data, coordinates), "tsne.csv", "Download tsne positions")
//DOM.download(serialize(data, force), "force.csv", "Download force positions")
{
const data = tf.randomUniform([2000,10]);
// Get a tsne optimizer
const tsneOpt = tf.tsne.tsne(data);
// Compute a T-SNE embedding, returns a promise.
// Runs for 1000 iterations by default.
tsneOpt.compute().then(() => {
// tsne.coordinate returns a *tensor* with x, y coordinates of
// the embedded data.
const coordinates = tsneOpt.coordinates();
coordinates.print();
}) ;
}
// tsne = require('@tensorflow/tfjs-tsne')
// tsne = require("http://localhost:8000/dist/tfjs-tsne.js")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
