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Pure python implementation of product quantization for nearest neighbor search

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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, 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[: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)



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