%global __brp_check_rpaths %{nil} %global packname node2vec %global packver 0.1.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.1.0 Release: 1%{?dist}%{?buildtag} Summary: Algorithmic Framework for Representational Learning on Graphs License: GPL (>= 3) URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 2.10 Requires: R-core >= 2.10 BuildArch: noarch BuildRequires: R-CRAN-data.table BuildRequires: R-CRAN-igraph BuildRequires: R-CRAN-word2vec BuildRequires: R-CRAN-rlist BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-vctrs BuildRequires: R-CRAN-vegan Requires: R-CRAN-data.table Requires: R-CRAN-igraph Requires: R-CRAN-word2vec Requires: R-CRAN-rlist Requires: R-CRAN-dplyr Requires: R-CRAN-vctrs Requires: R-CRAN-vegan %description Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at . %prep %setup -q -c -n %{packname} # fix end of executable files find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; # prevent binary stripping [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true # don't allow local prefix in executable scripts find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \; %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 # remove buildroot from installed files find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}