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%global _empty_manifest_terminate_build 0
Name: python-bayespy
Version: 0.5.26
Release: 1
Summary: Variational Bayesian inference tools for Python
License: MIT License
URL: http://bayespy.org
Source0: https://mirrors.aliyun.com/pypi/web/packages/76/1a/207c285e76cfc57cd22b91041e0c409dfda57b1bea52056b02d5b666c748/bayespy-0.5.26.tar.gz
BuildArch: noarch
%description
BayesPy provides tools for Bayesian inference with Python. The user
constructs a model as a Bayesian network, observes data and runs
posterior inference. The goal is to provide a tool which is
efficient, flexible and extendable enough for expert use but also
accessible for more casual users.
Currently, only variational Bayesian inference for
conjugate-exponential family (variational message passing) has been
implemented. Future work includes variational approximations for
other types of distributions and possibly other approximate inference
methods such as expectation propagation, Laplace approximations,
Markov chain Monte Carlo (MCMC) and other methods. Contributions are
welcome.
%package -n python3-bayespy
Summary: Variational Bayesian inference tools for Python
Provides: python-bayespy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-bayespy
BayesPy provides tools for Bayesian inference with Python. The user
constructs a model as a Bayesian network, observes data and runs
posterior inference. The goal is to provide a tool which is
efficient, flexible and extendable enough for expert use but also
accessible for more casual users.
Currently, only variational Bayesian inference for
conjugate-exponential family (variational message passing) has been
implemented. Future work includes variational approximations for
other types of distributions and possibly other approximate inference
methods such as expectation propagation, Laplace approximations,
Markov chain Monte Carlo (MCMC) and other methods. Contributions are
welcome.
%package help
Summary: Development documents and examples for bayespy
Provides: python3-bayespy-doc
%description help
BayesPy provides tools for Bayesian inference with Python. The user
constructs a model as a Bayesian network, observes data and runs
posterior inference. The goal is to provide a tool which is
efficient, flexible and extendable enough for expert use but also
accessible for more casual users.
Currently, only variational Bayesian inference for
conjugate-exponential family (variational message passing) has been
implemented. Future work includes variational approximations for
other types of distributions and possibly other approximate inference
methods such as expectation propagation, Laplace approximations,
Markov chain Monte Carlo (MCMC) and other methods. Contributions are
welcome.
%prep
%autosetup -n bayespy-0.5.26
%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-bayespy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.26-1
- Package Spec generated
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