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%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<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // standard error of effect estimates
}
parameters {
real mu; // population treatment effect
real<lower=0> 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<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // standard error of effect estimates
}
parameters {
real mu; // population treatment effect
real<lower=0> 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<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // standard error of effect estimates
}
parameters {
real mu; // population treatment effect
real<lower=0> 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
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.6.0-1
- Package Spec generated
|
