File R-RGAN.spec of Package R-RGAN
# Automatically generated by CRAN2OBS
#
# Spec file for package RGAN
# This file is auto-generated using information in the package source,
# esp. Description and Summary. Improvements in that area should be
# discussed with upstream.
#
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%global packname RGAN
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 0.1.1
Release: 0
Summary: Generative Adversarial Nets (GAN) in R
Group: Development/Libraries/Other
License: MIT + file LICENSE
URL: http://cran.r-project.org/web/packages/%{packname}
Source: RGAN_0.1.1.tar.gz
Requires: R-base
Requires: R-cli
Requires: R-torch
Requires: R-viridis
Requires: R-Rcpp
Requires: R-R6
Requires: R-withr
Requires: R-rlang
Requires: R-bit64
Requires: R-magrittr
Requires: R-coro
Requires: R-callr
Requires: R-glue
Requires: R-desc
Requires: R-safetensors
Requires: R-jsonlite
Requires: R-scales
Requires: R-viridisLite
Requires: R-ggplot2
Requires: R-gridExtra
Requires: R-bit
Requires: R-processx
Requires: R-gtable
Requires: R-isoband
Requires: R-lifecycle
Requires: R-S7
Requires: R-vctrs
Requires: R-farver
Requires: R-labeling
Requires: R-RColorBrewer
Requires: R-cpp11
Requires: R-ps
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse}
# # Three others commonly needed
# BuildRequires: tex(ae.sty)
# BuildRequires: tex(fancyvrb.sty)
# BuildRequires: tex(inconsolata.sty)
# BuildRequires: tex(natbib.sty)
# %else
# BuildRequires: texlive
# %endif
# BuildRequires: texinfo
BuildRequires: fdupes
BuildRequires: R-base
BuildRequires: R-cli
BuildRequires: R-torch
BuildRequires: R-viridis
BuildRequires: R-Rcpp-devel
BuildRequires: R-R6
BuildRequires: R-withr
BuildRequires: R-rlang
BuildRequires: R-bit64
BuildRequires: R-magrittr
BuildRequires: R-coro
BuildRequires: R-callr
BuildRequires: R-glue
BuildRequires: R-desc
BuildRequires: R-safetensors
BuildRequires: R-jsonlite
BuildRequires: R-scales
BuildRequires: R-viridisLite
BuildRequires: R-ggplot2
BuildRequires: R-gridExtra
BuildRequires: R-bit
BuildRequires: R-processx
BuildRequires: R-gtable
BuildRequires: R-isoband
BuildRequires: R-lifecycle
BuildRequires: R-S7
BuildRequires: R-vctrs
BuildRequires: R-farver
BuildRequires: R-labeling
BuildRequires: R-RColorBrewer
BuildRequires: R-cpp11-devel
BuildRequires: R-ps
%description
An easy way to get started with Generative Adversarial Nets (GAN) in R.
The GAN algorithm was initially described by Goodfellow et al. 2014
<https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>.
A GAN can be used to learn the joint distribution of complex data by
comparison. A GAN consists of two neural networks a Generator and a
Discriminator, where the two neural networks play an adversarial
minimax game. Built-in GAN models make the training of GANs in R
possible in one line and make it easy to experiment with different
design choices (e.g. different network architectures, value functions,
optimizers). The built-in GAN models work with tabular data (e.g. to
produce synthetic data) and image data. Methods to post-process the
output of GAN models to enhance the quality of samples are available.
%prep
%setup -q -c -n %{packname}
# the next line is needed, because we build without --clean in between two packages
rm -rf ~/.R
%build
%install
mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
%fdupes -s %{buildroot}%{rlibdir}
#%%check
#%%{_bindir}/R CMD check %%{packname}
%files
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog