Quick Start

Basic Usage

import jax
import jax.numpy as jnp
from bayes_hdc import MAP

model = MAP.create(dimensions=10000)
key = jax.random.PRNGKey(42)
k1, k2 = jax.random.split(key)

x = model.random(k1, (10000,))
y = model.random(k2, (10000,))

bound = model.bind(x, y)
bundled = model.bundle(jnp.stack([x, y]), axis=0)
sim = model.similarity(x, y)

Classification Pipeline

import jax
from bayes_hdc import MAP, RandomEncoder, CentroidClassifier

model = MAP.create(dimensions=10000)
key = jax.random.PRNGKey(42)

encoder = RandomEncoder.create(
    num_features=20, num_values=10, dimensions=10000,
    vsa_model=model, key=key,
)
classifier = CentroidClassifier.create(
    num_classes=5, dimensions=10000, vsa_model=model,
)

data = jax.random.randint(key, (100, 20), 0, 10)
labels = jax.random.randint(key, (100,), 0, 5)
encoded = encoder.encode_batch(data)
classifier = classifier.fit(encoded, labels)
accuracy = classifier.score(encoded, labels)

Key Concepts

  • Hypervectors: High-dimensional vectors (typically 10,000 dimensions)

  • Binding: Combines two hypervectors into a dissimilar result

  • Bundling: Superposes multiple hypervectors into a similar result

  • Similarity: Measures relatedness between hypervectors