%global _empty_manifest_terminate_build 0 Name: python-PyLBFGS Version: 0.2.0.14 Release: 1 Summary: LBFGS and OWL-QN optimization algorithms License: MIT License URL: https://github.com/dedupeio/pylbfgs Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e2/7e/e014b3394a547148618764d25fd173835de1de80e3b11356d052ac80ef4f/PyLBFGS-0.2.0.14.tar.gz Requires: python3-numpy %description This is a Python wrapper around Naoaki Okazaki (chokkan)'s liblbfgs_ library of quasi-Newton optimization routines (limited memory BFGS and OWL-QN). This package aims to provide a cleaner interface to the LBFGS algorithm than is currently available in SciPy_, and to provide the OWL-QN algorithm to Python users. Part of the Dedupe.io_ cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data. %package -n python3-PyLBFGS Summary: LBFGS and OWL-QN optimization algorithms Provides: python-PyLBFGS BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-PyLBFGS This is a Python wrapper around Naoaki Okazaki (chokkan)'s liblbfgs_ library of quasi-Newton optimization routines (limited memory BFGS and OWL-QN). This package aims to provide a cleaner interface to the LBFGS algorithm than is currently available in SciPy_, and to provide the OWL-QN algorithm to Python users. Part of the Dedupe.io_ cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data. %package help Summary: Development documents and examples for PyLBFGS Provides: python3-PyLBFGS-doc %description help This is a Python wrapper around Naoaki Okazaki (chokkan)'s liblbfgs_ library of quasi-Newton optimization routines (limited memory BFGS and OWL-QN). This package aims to provide a cleaner interface to the LBFGS algorithm than is currently available in SciPy_, and to provide the OWL-QN algorithm to Python users. Part of the Dedupe.io_ cloud service and open source toolset for de-duplicating and finding fuzzy matches in your data. %prep %autosetup -n PyLBFGS-0.2.0.14 %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-PyLBFGS -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 0.2.0.14-1 - Package Spec generated