%global _empty_manifest_terminate_build 0 Name: python-pystan Version: 3.6.0 Release: 1 Summary: Python interface to Stan, a package for Bayesian inference License: ISC URL: https://mc-stan.org Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b4/34/c82f306600df2812996689d1d3fc5acb5fa1fd627ad895ede29cd3ee9605/pystan-3.6.0.tar.gz BuildArch: noarch Requires: python3-aiohttp Requires: python3-httpstan Requires: python3-pysimdjson Requires: python3-numpy Requires: python3-clikit Requires: python3-setuptools %description Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. import stan schools_code = """ data { int J; // number of schools real y[J]; // estimated treatment effects real sigma[J]; // standard error of effect estimates } parameters { real mu; // population treatment effect real tau; // standard deviation in treatment effects vector[J] eta; // unscaled deviation from mu by school } transformed parameters { vector[J] theta = mu + tau * eta; // school treatment effects } model { target += normal_lpdf(eta | 0, 1); // prior log-density target += normal_lpdf(y | theta, sigma); // log-likelihood } """ schools_data = {"J": 8, "y": [28, 8, -3, 7, -1, 1, 18, 12], "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} posterior = stan.build(schools_code, data=schools_data) fit = posterior.sample(num_chains=4, num_samples=1000) eta = fit["eta"] # array with shape (8, 4000) df = fit.to_frame() # pandas `DataFrame` %package -n python3-pystan Summary: Python interface to Stan, a package for Bayesian inference Provides: python-pystan BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pystan Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. import stan schools_code = """ data { int J; // number of schools real y[J]; // estimated treatment effects real sigma[J]; // standard error of effect estimates } parameters { real mu; // population treatment effect real tau; // standard deviation in treatment effects vector[J] eta; // unscaled deviation from mu by school } transformed parameters { vector[J] theta = mu + tau * eta; // school treatment effects } model { target += normal_lpdf(eta | 0, 1); // prior log-density target += normal_lpdf(y | theta, sigma); // log-likelihood } """ schools_data = {"J": 8, "y": [28, 8, -3, 7, -1, 1, 18, 12], "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} posterior = stan.build(schools_code, data=schools_data) fit = posterior.sample(num_chains=4, num_samples=1000) eta = fit["eta"] # array with shape (8, 4000) df = fit.to_frame() # pandas `DataFrame` %package help Summary: Development documents and examples for pystan Provides: python3-pystan-doc %description help Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. import stan schools_code = """ data { int J; // number of schools real y[J]; // estimated treatment effects real sigma[J]; // standard error of effect estimates } parameters { real mu; // population treatment effect real tau; // standard deviation in treatment effects vector[J] eta; // unscaled deviation from mu by school } transformed parameters { vector[J] theta = mu + tau * eta; // school treatment effects } model { target += normal_lpdf(eta | 0, 1); // prior log-density target += normal_lpdf(y | theta, sigma); // log-likelihood } """ schools_data = {"J": 8, "y": [28, 8, -3, 7, -1, 1, 18, 12], "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} posterior = stan.build(schools_code, data=schools_data) fit = posterior.sample(num_chains=4, num_samples=1000) eta = fit["eta"] # array with shape (8, 4000) df = fit.to_frame() # pandas `DataFrame` %prep %autosetup -n pystan-3.6.0 %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-pystan -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 3.6.0-1 - Package Spec generated