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%global _empty_manifest_terminate_build 0
Name: python-pypmc
Version: 1.2
Release: 1
Summary: A toolkit for adaptive importance sampling featuring implementations of variational Bayes, population Monte Carlo, and Markov chains.
License: GPLv2
URL: https://github.com/pypmc/pypmc
Source0: https://mirrors.aliyun.com/pypi/web/packages/9f/5c/79342ebacdf9005a1461c2d023338e9573eeb7f2ec9ea10797d0ca10a192/pypmc-1.2.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-mpi4py
Requires: python3-matplotlib
Requires: python3-nose
%description
``pypmc`` is a python package focusing on adaptive importance
sampling. It can be used for integration and sampling from a
user-defined target density. A typical application is Bayesian
inference, where one wants to sample from the posterior to marginalize
over parameters and to compute the evidence. The key idea is to create
a good proposal density by adapting a mixture of Gaussian or student's
t components to the target density. The package is able to efficiently
integrate multimodal functions in up to about 30-40 dimensions at the
level of 1% accuracy or less. For many problems, this is achieved
without requiring any manual input from the user about details of the
function. Importance sampling supports parallelization on multiple
machines via ``mpi4py``.
Useful tools that can be used stand-alone include:
* importance sampling (sampling & integration)
* adaptive Markov chain Monte Carlo (sampling)
* variational Bayes (clustering)
* population Monte Carlo (clustering)
%package -n python3-pypmc
Summary: A toolkit for adaptive importance sampling featuring implementations of variational Bayes, population Monte Carlo, and Markov chains.
Provides: python-pypmc
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pypmc
``pypmc`` is a python package focusing on adaptive importance
sampling. It can be used for integration and sampling from a
user-defined target density. A typical application is Bayesian
inference, where one wants to sample from the posterior to marginalize
over parameters and to compute the evidence. The key idea is to create
a good proposal density by adapting a mixture of Gaussian or student's
t components to the target density. The package is able to efficiently
integrate multimodal functions in up to about 30-40 dimensions at the
level of 1% accuracy or less. For many problems, this is achieved
without requiring any manual input from the user about details of the
function. Importance sampling supports parallelization on multiple
machines via ``mpi4py``.
Useful tools that can be used stand-alone include:
* importance sampling (sampling & integration)
* adaptive Markov chain Monte Carlo (sampling)
* variational Bayes (clustering)
* population Monte Carlo (clustering)
%package help
Summary: Development documents and examples for pypmc
Provides: python3-pypmc-doc
%description help
``pypmc`` is a python package focusing on adaptive importance
sampling. It can be used for integration and sampling from a
user-defined target density. A typical application is Bayesian
inference, where one wants to sample from the posterior to marginalize
over parameters and to compute the evidence. The key idea is to create
a good proposal density by adapting a mixture of Gaussian or student's
t components to the target density. The package is able to efficiently
integrate multimodal functions in up to about 30-40 dimensions at the
level of 1% accuracy or less. For many problems, this is achieved
without requiring any manual input from the user about details of the
function. Importance sampling supports parallelization on multiple
machines via ``mpi4py``.
Useful tools that can be used stand-alone include:
* importance sampling (sampling & integration)
* adaptive Markov chain Monte Carlo (sampling)
* variational Bayes (clustering)
* population Monte Carlo (clustering)
%prep
%autosetup -n pypmc-1.2
%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-pypmc -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2-1
- Package Spec generated
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