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%global _empty_manifest_terminate_build 0
Name: python-lea
Version: 3.4.4
Release: 1
Summary: Discrete probability distributions in Python
License: LGPL
URL: http://bitbucket.org/piedenis/lea
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/88/1d/bcd296e04f93d15eed58959a3209256252c8d0265c02ab343afa6e71cb15/lea-3.4.4.tar.gz
BuildArch: noarch
%description
Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
To install Lea 3.4.4, type the following command:
::
pip install lea==3.4.4
Please go on Lea project page (beside) for a comprehensive documentation.
%package -n python3-lea
Summary: Discrete probability distributions in Python
Provides: python-lea
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-lea
Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
To install Lea 3.4.4, type the following command:
::
pip install lea==3.4.4
Please go on Lea project page (beside) for a comprehensive documentation.
%package help
Summary: Development documents and examples for lea
Provides: python3-lea-doc
%description help
Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
To install Lea 3.4.4, type the following command:
::
pip install lea==3.4.4
Please go on Lea project page (beside) for a comprehensive documentation.
%prep
%autosetup -n lea-3.4.4
%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-lea -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.4.4-1
- Package Spec generated
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