This is the wasm version of the bhtsne crate.

Features

• Harnesses multi-threading capabilities through wasm-bindgen-rayon.
• Allows passing t-SNE hyperparameters through a JavaScript object, where you only need to include the parameters you want to change from the defaults. If you don't specify any, default values are used.
• Supports running the algorithm in iterations, enabling progressive refinement of the embedding
• Supports both Float32Array and Float64Array for data input

Requirements

To use the multithreading feature, you need to enable SharedArrayBuffer on the Web. As stated in the wasm-bindgen-rayon readme:

In order to use SharedArrayBuffer on the Web, you need to enable cross-origin isolation policies. Check out the linked article for details.

Installation

Install the wasm-bhtsne npm package:

npm i wasm-bhtsne

Example

import { threads } from 'wasm-feature-detect';

function createRandomMatrix(rows, columns) {
    return Array.from({ length: rows }, () =>
        Array.from({ length: columns }, () => Math.random())
    );
}

(async function initMultiThread() {
    const multiThread = await import('./pkg-parallel/wasm_bhtsne.js');
    await multiThread.default();
    if (await threads()) {
        console.log("Browser supports threads");
        await multiThread.initThreadPool(navigator.hardwareConcurrency);
    } else {
        console.log("Browser does not support threads");
    }

    Object.assign(document.getElementById("wasm-bhtsne"), {
        async onclick() {

            // create random points and dimensions
            const data = createRandomMatrix(5000, 512);

            // Example of setting hyperparameters
            const opt = {
                learning_rate: 150.0,
                perplexity: 30.0,
                theta: 0.6
            };
            
            // let tsne_encoder = new multiThread.bhtSNEf64(data, opt);
            // or
            let tsne_encoder = new multiThread.bhtSNEf32(data, opt);
            let compressed_vectors;

            for (let i = 0; i < 1000; i++) {
                compressed_vectors = tsne_encoder.step(1)
                /* …do something with `compressed_vectors`… */
            }

            console.log("Compressed Vectors:", compressed_vectors);
        },
        disabled: false
    });
})();

Hyperparameters

Here is a list of hyperparameters that can be set in the JavaScript object, along with their default values and descriptions:

• learning_rate (default: 200.0): controls the step size during the optimization.

• momentum (default: 0.5): helps accelerate gradients vectors in the right directions, thus leading to faster converging.

• final_momentum (default: 0.8): momentum value used after a certain number of iterations.

• momentum_switch_epoch (default: 250): the epoch after which the algorithm switches to final_momentum for the map update.

• stop_lying_epoch (default: 250): the epoch after which the P distribution values become true. For epochs < stop_lying_epoch, the values of the P distribution are multiplied by a factor equal to 12.0.

• theta (default: 0.5): Determines the accuracy of the approximation. Larger values increase the speed but decrease accuracy. Must be strictly greater than 0.0.

• embedding_dim (default: 2): the dimensionality of the embedding space.

• perplexity (default: 20.0): the perplexity value. It determines the balance between local and global aspects of the data. A good value lies between 5.0 and 50.0.