%global _empty_manifest_terminate_build 0 Name: python-nanopq Version: 0.1.11 Release: 1 Summary: Pure python implementation of product quantization for nearest neighbor search License: MIT URL: https://github.com/matsui528/nanopq Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c4/bb/187f489194c27d3dd233bd40081e1e7ba6c5899830428539664c7204a2d1/nanopq-0.1.11.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy %description # nanopq [![Build Status](https://github.com/matsui528/nanopq/actions/workflows/build.yml/badge.svg)](https://github.com/matsui528/nanopq/actions) [![Documentation Status](https://readthedocs.org/projects/nanopq/badge/?version=latest)](https://nanopq.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/nanopq.svg)](https://badge.fury.io/py/nanopq) [![Downloads](https://pepy.tech/badge/nanopq)](https://pepy.tech/project/nanopq) 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](https://nanopq.readthedocs.io/en/latest/index.html) - [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html) - [API](https://nanopq.readthedocs.io/en/latest/source/api.html) ## Example ```python 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 - [Yusuke Matsui](http://yusukematsui.me) ## Contributors - [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14)) - [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19)) ## Reference - [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ) - [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ) - [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ) - [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss)) - [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ) - [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb)) - [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq) - [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial) %package -n python3-nanopq Summary: Pure python implementation of product quantization for nearest neighbor search Provides: python-nanopq BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-nanopq # nanopq [![Build Status](https://github.com/matsui528/nanopq/actions/workflows/build.yml/badge.svg)](https://github.com/matsui528/nanopq/actions) [![Documentation Status](https://readthedocs.org/projects/nanopq/badge/?version=latest)](https://nanopq.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/nanopq.svg)](https://badge.fury.io/py/nanopq) [![Downloads](https://pepy.tech/badge/nanopq)](https://pepy.tech/project/nanopq) 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](https://nanopq.readthedocs.io/en/latest/index.html) - [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html) - [API](https://nanopq.readthedocs.io/en/latest/source/api.html) ## Example ```python 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 - [Yusuke Matsui](http://yusukematsui.me) ## Contributors - [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14)) - [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19)) ## Reference - [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ) - [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ) - [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ) - [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss)) - [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ) - [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb)) - [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq) - [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial) %package help Summary: Development documents and examples for nanopq Provides: python3-nanopq-doc %description help # nanopq [![Build Status](https://github.com/matsui528/nanopq/actions/workflows/build.yml/badge.svg)](https://github.com/matsui528/nanopq/actions) [![Documentation Status](https://readthedocs.org/projects/nanopq/badge/?version=latest)](https://nanopq.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/nanopq.svg)](https://badge.fury.io/py/nanopq) [![Downloads](https://pepy.tech/badge/nanopq)](https://pepy.tech/project/nanopq) 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](https://nanopq.readthedocs.io/en/latest/index.html) - [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html) - [API](https://nanopq.readthedocs.io/en/latest/source/api.html) ## Example ```python 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 - [Yusuke Matsui](http://yusukematsui.me) ## Contributors - [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14)) - [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19)) ## Reference - [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ) - [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ) - [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ) - [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss)) - [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ) - [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb)) - [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq) - [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial) %prep %autosetup -n nanopq-0.1.11 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-nanopq -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 25 2023 Python_Bot - 0.1.11-1 - Package Spec generated