NeuralNetwork.BernoulliRBM

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[][]

Example

const rbm = new BernoulliRBM({ nComponents: 2, nIter: 20 });
rbm.fit(data);
const h = rbm.transform(data);

Practical guide: BernoulliRBM in JavaScript and TypeScript

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

When to use BernoulliRBM

• 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.

Implementation workflow

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

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• BernoulliRBM JavaScript
• BernoulliRBM TypeScript
• BernoulliRBM browser machine learning
• @kanaries/ml BernoulliRBM