Skip to main content

Pure python implementation of product quantization for nearest neighbor search

Project description


Build Status Documentation Status PyPI version Downloads

Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.


You can install the package via pip. This library works with Python 3.5+ on linux.

pip install nanopq



import nanopq
import numpy as np

N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32)  # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32)  # a 128-dim query vector

# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)

# Train codewords

# Encode to PQ-codes
X_code = pq.encode(X)  # (10000, 8) with dtype=np.uint8

# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code 
dists = pq.dtable(query).adist(X_code)  # (10000, ) 



Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for nanopq, version 0.1.7
Filename, size File type Python version Upload date Hashes
Filename, size nanopq-0.1.7-py3-none-any.whl (9.2 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size nanopq-0.1.7.tar.gz (7.5 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page