%global _empty_manifest_terminate_build 0 Name: python-pqkmeans Version: 1.0.5 Release: 1 Summary: Fast and memory-efficient clustering License: MIT License URL: http://yusukematsui.me/project/pqkmeans/pqkmeans.html Source0: https://mirrors.nju.edu.cn/pypi/web/packages/18/0e/0f5437c5e6aa3780b8816c93ae9d21f1fea00339e16c1779485e9091fd14/pqkmeans-1.0.5.tar.gz BuildArch: noarch %description PQk-means [Matsui, Ogaki, Yamasaki, and Aizawa, ACMMM 17] is a Python library for efficient clustering of large-scale data. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain. For a comparison, we provide the ITQ encoding for the binary conversion and Binary k-means [Gong+, CVPR 15] for the clustering of binary codes. The library is written in C++ for the main algorithm with wrappers for Python. All encoding/clustering codes are compatible with scikit-learn. %package -n python3-pqkmeans Summary: Fast and memory-efficient clustering Provides: python-pqkmeans BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pqkmeans PQk-means [Matsui, Ogaki, Yamasaki, and Aizawa, ACMMM 17] is a Python library for efficient clustering of large-scale data. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain. For a comparison, we provide the ITQ encoding for the binary conversion and Binary k-means [Gong+, CVPR 15] for the clustering of binary codes. The library is written in C++ for the main algorithm with wrappers for Python. All encoding/clustering codes are compatible with scikit-learn. %package help Summary: Development documents and examples for pqkmeans Provides: python3-pqkmeans-doc %description help PQk-means [Matsui, Ogaki, Yamasaki, and Aizawa, ACMMM 17] is a Python library for efficient clustering of large-scale data. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain. For a comparison, we provide the ITQ encoding for the binary conversion and Binary k-means [Gong+, CVPR 15] for the clustering of binary codes. The library is written in C++ for the main algorithm with wrappers for Python. All encoding/clustering codes are compatible with scikit-learn. %prep %autosetup -n pqkmeans-1.0.5 %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-pqkmeans -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 1.0.5-1 - Package Spec generated