Skip to main content

Pure python implementation of product quantization for nearest neighbor search

Project description

nanopq

Documentation Status

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.

Installing

You can install the package via pip. This library works with Python 3.6+ on linux.

pip install nanopq

Documentation

Example

import nanopq
import numpy as np

N, D = 10000, 128
X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors
query = np.random.random((D,)).astype(np.float32)  # a 128-dim vector

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

# Train with the top 1000 vectors
pq.fit(X[:1000])

# 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)

Author

Reference

Project details


Download files

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

Source Distribution

nanopq-0.1.4.tar.gz (6.3 kB view hashes)

Uploaded source

Built Distribution

nanopq-0.1.4-py3-none-any.whl (6.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page