##
##  opennn.spec -- OpenPKG RPM Package Specification
##  Copyright (c) 2000-2025 OpenPKG Project <http://openpkg.org/>
##
##  Permission to use, copy, modify, and distribute this software for
##  any purpose with or without fee is hereby granted, provided that
##  the above copyright notice and this permission notice appear in all
##  copies.
##
##  THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
##  WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
##  MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
##  IN NO EVENT SHALL THE AUTHORS AND COPYRIGHT HOLDERS AND THEIR
##  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
##  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
##  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
##  USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
##  ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
##  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
##  OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
##  SUCH DAMAGE.
##

#   package version
%define       V_opkg   5.0.5
%define       V_dist   20210117

#   package information
Name:         opennn
Summary:      Neural Network Library
URL:          http://opennn.net/
Vendor:       Artelnics
Packager:     OpenPKG Project
Distribution: OpenPKG Community
Class:        EVAL
Group:        Algorithm
License:      LGPL
Version:      %{V_opkg}
Release:      20210117

#   list of sources
Source0:      http://download.openpkg.org/components/versioned/opennn/opennn-%{V_dist}.tar.xz

#   build information
BuildPreReq:  OpenPKG, openpkg >= 20160101, cmake
PreReq:       OpenPKG, openpkg >= 20160101

%description
    OpenNN is a software library written in C++ for predictive
    analytics. It implements neural networks, the most successful
    deep learning method. The main advantage of OpenNN is its high
    performance. This library outstands in terms of execution speed
    and memory allocation. It is constantly optimized and parallelized
    in order to maximize its efficiency. Some typical applications of
    OpenNN are function regression (modelling), pattern recognition
    (classification) and time series prediction (forecasting).

%track
    prog opennn:base = {
        version   = %{V_opkg}
        url       = https://github.com/Artelnics/opennn/releases
        regex     = v(__VER__)\.tar\.gz
    }
    prog opennn:snap = {
        version   = %{V_dist}
        url       = http://download.openpkg.org/components/versioned/opennn/
        regex     = opennn-(__VER__)\.tar\.xz
    }

%prep
    %setup -q -n opennn

%build
    mkdir build
    cd build
    cmake \
        -DCMAKE_BUILD_TYPE="Release" \
        -DCMAKE_INSTALL_PREFIX="%{l_prefix}" \
        -DCMAKE_C_COMPILER="%{l_cc}" \
        -DCMAKE_C_FLAGS="%{l_cflags} %{l_cppflags} -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-enum-compare" \
        -DCMAKE_EXE_LINKER_FLAGS="%{l_ldflags}" \
        -DCMAKE_CXX_COMPILER="%{l_cxx}" \
        -DCMAKE_CXX_FLAGS="%{l_cxxflags} -std=c++14 -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-enum-compare" \
        -DBUILD_SHARED_LIBS=OFF \
        -DOpenNN_BUILD_EXAMPLES=OFF \
        -DOpenNN_BUILD_BLANK=OFF \
        -DOpenNN_BUILD_TESTS=OFF \
        ..
    %{l_make} %{l_mflags -O}

%install
    %{l_shtool} mkdir -f -p -m 755 \
        $RPM_BUILD_ROOT%{l_prefix}/lib \
        $RPM_BUILD_ROOT%{l_prefix}/include/opennn
    %{l_shtool} install -c -m 644 \
        build/opennn/libopennn.a $RPM_BUILD_ROOT%{l_prefix}/lib/
    %{l_shtool} install -c -m 644 \
        opennn/*.h $RPM_BUILD_ROOT%{l_prefix}/include/opennn/
    %{l_rpmtool} files -v -ofiles -r$RPM_BUILD_ROOT %{l_files_std}

%files -f files

%clean

