%global _empty_manifest_terminate_build 0 Name: python-Bayesian2D Version: 0.3.1 Release: 1 Summary: Package used to find the maximum or minimum of any 2D function using Bayesian optimization License: MIT URL: https://github.com/JRaidal/Bayesian2D Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5d/db/829d2abe56768e55c3fcea23d520e92e0eff5013de88c7fbcbd08312bc89/Bayesian2D-0.3.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-datetime Requires: python3-scipy Requires: python3-sklearn Requires: python3-matplotlib %description # Bayesian2D This package implements Bayesian optimization in Python for any 2D function. It uses Gaussian regression to create a surrogate function and the Maximum Probability of Improvement aquisition function to pick points to evaluate, thus finding the specified extremum of the function in only a few hundred evaluations. # How to install The package can simply be installed with 'pip install Bayesian2D'. # How to use The package contains two directories- tools and tests. The tools folder contains all the separate python functions used by the algorithm, with the Bayesian2D function being the main function of the package. To optimize your function just import 'from Bayesian2D.tools import Bayesian2D'. The function takes as an input the function you wish to optimize and the bounds in which you wish to search for the extremum (there are a few built in named functions such as 'Beale' or 'Ackley' with the Rosenbrock function being the default but custom functions can also be inserted). The function also requires you to specify the number of initial points evaluated, the number of optimization cycles run, the number of random points evaluated by the surrogate function each cycle, the exploration constant and whether you want to find the maximum or minimum. # Testing Unit tests for all the functions used can be found in the aforementioned tests directory. %package -n python3-Bayesian2D Summary: Package used to find the maximum or minimum of any 2D function using Bayesian optimization Provides: python-Bayesian2D BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-Bayesian2D # Bayesian2D This package implements Bayesian optimization in Python for any 2D function. It uses Gaussian regression to create a surrogate function and the Maximum Probability of Improvement aquisition function to pick points to evaluate, thus finding the specified extremum of the function in only a few hundred evaluations. # How to install The package can simply be installed with 'pip install Bayesian2D'. # How to use The package contains two directories- tools and tests. The tools folder contains all the separate python functions used by the algorithm, with the Bayesian2D function being the main function of the package. To optimize your function just import 'from Bayesian2D.tools import Bayesian2D'. The function takes as an input the function you wish to optimize and the bounds in which you wish to search for the extremum (there are a few built in named functions such as 'Beale' or 'Ackley' with the Rosenbrock function being the default but custom functions can also be inserted). The function also requires you to specify the number of initial points evaluated, the number of optimization cycles run, the number of random points evaluated by the surrogate function each cycle, the exploration constant and whether you want to find the maximum or minimum. # Testing Unit tests for all the functions used can be found in the aforementioned tests directory. %package help Summary: Development documents and examples for Bayesian2D Provides: python3-Bayesian2D-doc %description help # Bayesian2D This package implements Bayesian optimization in Python for any 2D function. It uses Gaussian regression to create a surrogate function and the Maximum Probability of Improvement aquisition function to pick points to evaluate, thus finding the specified extremum of the function in only a few hundred evaluations. # How to install The package can simply be installed with 'pip install Bayesian2D'. # How to use The package contains two directories- tools and tests. The tools folder contains all the separate python functions used by the algorithm, with the Bayesian2D function being the main function of the package. To optimize your function just import 'from Bayesian2D.tools import Bayesian2D'. The function takes as an input the function you wish to optimize and the bounds in which you wish to search for the extremum (there are a few built in named functions such as 'Beale' or 'Ackley' with the Rosenbrock function being the default but custom functions can also be inserted). The function also requires you to specify the number of initial points evaluated, the number of optimization cycles run, the number of random points evaluated by the surrogate function each cycle, the exploration constant and whether you want to find the maximum or minimum. # Testing Unit tests for all the functions used can be found in the aforementioned tests directory. %prep %autosetup -n Bayesian2D-0.3.1 %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-Bayesian2D -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.3.1-1 - Package Spec generated