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%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 <Python_Bot@openeuler.org> - 1.0.5-1
- Package Spec generated
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