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:
``shell
npm i wasm-bhtsne
`

Example

`javascript
import { threads } from 'wasm-feature-detect';
import init, { initThreadPool, bhtSNEf32 } from 'wasm-bhtsne';

// Configuration
const config = {
n_samples: 5000,
n_dims: 512,
iterations: 1000
};

// Helper to generate example data (Rust expects Vec>)
const generateData = (rows, cols) =>
Array.from({ length: rows }, () =>
Array.from({ length: cols }, () => Math.random())
);

(async () => {
// Initialize WASM module
await init();

// Check and initialize threads
if (await threads()) {
console.log(Browser supports threads. Using ${navigator.hardwareConcurrency} cores.);
await initThreadPool(navigator.hardwareConcurrency);
} else {
console.log("Browser does not support threads.");
return;
}

// Set hyperparameters
const opt = {
learning_rate: 150.0,
perplexity: 30.0,
theta: 0.5,
};

try {
const data = generateData(config.n_samples, config.n_dims);

// Instantiate the encoder
const tsneEncoder = new bhtSNEf32(data, opt);

let embedding;

// Run the algorithm
for (let i = 0; i < config.iterations; i++) {
embedding = tsneEncoder.step(1);
}

console.log("Final Embedding:", embedding);

} catch (e) {
console.error("Error during execution:", e);
}
})();
`

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.