hypercube-hopfield 0.1.1

Python bindings for HypercubeHopfield: modern Hopfield associative memory on hypercube graphs

HypercubeHopfield: Modern Hopfield Associative Memory

Pattern storage and recall on Boolean hypercube graphs, with exponential capacity and softmax attention over sparse Hamming-ball neighborhoods.

Installation

pip install hypercube-hopfield

Quick Start

import numpy as np
import hypercube_hopfield as hh

# Create a network with 256 neurons (dim=8, N=2^8)
net = hh.HopfieldNetwork(dim=8, seed=42)

# Store some patterns
patterns = np.random.randn(10, net.num_vertices).astype(np.float32)
net.store_patterns(patterns)

# Recall from a noisy cue
cue = patterns[0] + np.random.randn(net.num_vertices).astype(np.float32) * 0.3
result = net.recall(cue)
print(f"Converged: {result.converged}, steps: {result.steps}")

Features

• Explicit pattern storage with exponential capacity
• Softmax attention over sparse Hamming-ball neighborhoods
• Two update modes: Sync (deterministic, GPU-portable) and Async (guaranteed energy descent)
• Pickle support for saving/loading networks
• NumPy integration with automatic float32 conversion

Documentation

See Python SDK Reference for the full API.