%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 * Tue May 30 2023 Python_Bot - 3.4.4-1 - Package Spec generated