Pure python implementation of product quantization for nearest neighbor search


Keywords
approximate-nearest-neighbor-search, data-compression, nearest-neighbor-search, product-quantization
License
MIT
Install
pip install nanopq==0.1.10

Documentation

nanopq

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.

Installing

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

pip install nanopq

Documentation

Example

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
pq.fit(Xt)

# 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, ) 

Author

Reference