%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  MFF
%global packver   0.2.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.2.0
Release:          1%{?dist}%{?buildtag}
Summary:          Meta Fuzzy Functions

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


BuildRequires:    R-devel
Requires:         R-core
BuildArch:        noarch
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-CRAN-randomForest 
BuildRequires:    R-CRAN-xgboost 
BuildRequires:    R-CRAN-lightgbm 
BuildRequires:    R-CRAN-e1071 
BuildRequires:    R-CRAN-ppclust 
BuildRequires:    R-CRAN-doParallel 
BuildRequires:    R-CRAN-foreach 
BuildRequires:    R-parallel 
Requires:         R-CRAN-glmnet 
Requires:         R-CRAN-randomForest 
Requires:         R-CRAN-xgboost 
Requires:         R-CRAN-lightgbm 
Requires:         R-CRAN-e1071 
Requires:         R-CRAN-ppclust 
Requires:         R-CRAN-doParallel 
Requires:         R-CRAN-foreach 
Requires:         R-parallel 

%description
Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026)
<doi:10.1016/j.asoc.2026.114592> by aggregating predictions from multiple
base learners using membership weights learned in the prediction space of
validation set. The package supports fuzzy and crisp meta-ensemble
structures via Fuzzy C-Means (FCM) Tak (2018)
<doi:10.1016/j.asoc.2018.08.009>, Possibilistic FCM (PFCM) Tak (2021)
<doi:10.1016/j.ins.2021.01.024>, Gustafson–Kessel (GK) clustering, and
k-means, and provides a workflow to (i) generate validation/test
prediction matrices from common regression learners (linear and penalized
regression via 'glmnet', random forests, gradient boosting with 'xgboost'
and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute
membership-based weights, (iii) tune clustering-related hyperparameters
(number of clusters/functions, fuzziness exponent, possibilistic
regularization) via grid search on validation loss, and (iv) predict on
new/test prediction matrices and evaluate performance using standard
regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables
flexible, interpretable ensemble regression where different base models
contribute to different meta components according to learned memberships.

%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}