%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  DatabionicSwarm
%global packver   2.0.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          2.0.0
Release:          1%{?dist}%{?buildtag}
Summary:          Swarm Intelligence for Self-Organized Clustering

License:          GPL-3
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 3.0
Requires:         R-core >= 3.0
BuildRequires:    R-CRAN-RcppParallel >= 5.1.4
BuildRequires:    R-CRAN-Rcpp >= 1.0.8
BuildRequires:    R-CRAN-deldir 
BuildRequires:    R-CRAN-GeneralizedUmatrix 
BuildRequires:    R-CRAN-ABCanalysis 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-RcppArmadillo 
Requires:         R-CRAN-RcppParallel >= 5.1.4
Requires:         R-CRAN-Rcpp >= 1.0.8
Requires:         R-CRAN-deldir 
Requires:         R-CRAN-GeneralizedUmatrix 
Requires:         R-CRAN-ABCanalysis 
Requires:         R-CRAN-ggplot2 

%description
Algorithms implementing populations of agents that interact with one
another and sense their environment may exhibit emergent behavior such as
self-organization and swarm intelligence. Here, a swarm system called
Databionic swarm (DBS) is introduced which was published in Thrun, M.C.,
Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020),
Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able
to adapt itself to structures of high-dimensional data such as natural
clusters characterized by distance and/or density based structures in the
data space. The first module is the parameter-free projection method
called Pswarm (Pswarm()), which exploits the concepts of self-organization
and emergence, game theory, swarm intelligence and symmetry
considerations. The second module is the parameter-free high-dimensional
data visualization technique, which generates projected points on the
topographic map with hypsometric tints defined by the generalized U-matrix
(GeneratePswarmVisualization()). The third module is the clustering method
itself with non-critical parameters (DBSclustering()). Clustering can be
verified by the visualization and vice versa. The term DBS refers to the
method as a whole. It enables even a non-professional in the field of data
mining to apply its algorithms for visualization and/or clustering to data
sets with completely different structures drawn from diverse research
fields. The comparison to common projection methods can be found in the
book of Thrun, M.C.: "Projection Based Clustering through
Self-Organization and Swarm Intelligence" (2018)
<DOI:10.1007/978-3-658-20540-9>.

%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
[ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \
  sed -i 's@-g0@@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}