Bernoulli RBM in JavaScript with @kanaries/ml

Learn what Bernoulli RBM does, when to use it, and how to run BernoulliRBM in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications.

Algorithm overview

BernoulliRBM learns latent binary representations that can improve downstream supervised model quality.

This algorithm is especially useful when:

You work with binary-valued or binarized feature inputs.
Feature learning can improve separability for a later classifier.
You need compact latent features in a JavaScript pipeline.

@kanaries/ml provides Bernoulli RBM in JavaScript for feature learning workflows where latent binary representations are useful before a downstream classifier or recommender. This is relevant for experimentation-heavy products and Node.js pipelines that want learned hidden features without leaving TypeScript.

Because RBMs are usually part of a broader preprocessing or exploration pipeline, keeping them in JS makes it easier to chain transformation, visualization, and downstream modeling in one place.

Quick start

BernoulliRBM in Python vs JavaScript / TypeScript

If you searched for "BernoulliRBM in JavaScript" or "BernoulliRBM in TypeScript", this section maps the familiar scikit-learn call to the equivalent @kanaries/ml usage for browser and Node.js runtimes.

Python

scikit-learn

from sklearn.neural_network import BernoulliRBM

X = [[1, 0, 1], [1, 1, 0], [0, 1, 1], [0, 0, 1]]

rbm = BernoulliRBM(n_components=2, learning_rate=0.1, batch_size=2, n_iter=20, random_state=0)
hidden = rbm.fit_transform(X)

JavaScript / TypeScript

@kanaries/ml

import { NeuralNetwork } from '@kanaries/ml';

const X = [[1, 0, 1], [1, 1, 0], [0, 1, 1], [0, 0, 1]];

const rbm = new NeuralNetwork.BernoulliRBM({ nComponents: 2, learningRate: 0.1, batchSize: 2, nIter: 20 });
const hidden = rbm.fitTransform(X);

Quick JavaScript example

import { NeuralNetwork } from '@kanaries/ml';

const X = [[1, 0, 1], [1, 1, 0], [0, 1, 1], [0, 0, 1]];

const rbm = new NeuralNetwork.BernoulliRBM({ nComponents: 2, learningRate: 0.1, batchSize: 2, nIter: 20 });
const hidden = rbm.fitTransform(X);

Detailed API reference

constructor(
    nComponents: number = 256,
    learningRate: number = 0.1,
    batchSize: number = 10,
    nIter: number = 10
)

A Restricted Boltzmann Machine with binary visible units and hidden units. The model is trained with contrastive divergence.

Methods

fit(X: number[][]): void
partialFit(X: number[][]): void
transform(X: number[][]): number[][]
fitTransform(X: number[][]): number[][]
gibbs(V: number[][]): number[][]

Implementation workflow

Prepare binary feature matrix and configure hidden-unit size.
Fit RBM and generate transformed latent representations.
Train downstream models on latent features and compare lift.

JavaScript deployment notes

Use Bernoulli RBM when your inputs are binary or can be meaningfully binarized.
Treat it as a representation-learning step and compare downstream lift against raw features.
For frontend demos or educational tools, keep datasets modest because iterative training can still be expensive.

Bernoulli RBM