%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 - 1.2-1 - Package Spec generated