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104 lines
3.7 KiB
104 lines
3.7 KiB
## |
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## opennn.spec -- OpenPKG RPM Package Specification |
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## Copyright (c) 2000-2022 OpenPKG Project <http://openpkg.org/> |
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## |
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## Permission to use, copy, modify, and distribute this software for |
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## any purpose with or without fee is hereby granted, provided that |
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## the above copyright notice and this permission notice appear in all |
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## copies. |
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## |
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## THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED |
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## WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF |
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## MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. |
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## IN NO EVENT SHALL THE AUTHORS AND COPYRIGHT HOLDERS AND THEIR |
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## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, |
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## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT |
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## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF |
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## USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND |
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## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, |
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## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT |
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## OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF |
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## SUCH DAMAGE. |
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## |
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# package version |
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%define V_opkg 5.0.5 |
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%define V_dist 20210117 |
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# package information |
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Name: opennn |
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Summary: Neural Network Library |
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URL: http://opennn.net/ |
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Vendor: Artelnics |
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Packager: OpenPKG Project |
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Distribution: OpenPKG Community |
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Class: EVAL |
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Group: Algorithm |
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License: LGPL |
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Version: %{V_opkg} |
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Release: 20210117 |
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# list of sources |
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Source0: http://download.openpkg.org/components/versioned/opennn/opennn-%{V_dist}.tar.xz |
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# build information |
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BuildPreReq: OpenPKG, openpkg >= 20160101, cmake |
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PreReq: OpenPKG, openpkg >= 20160101 |
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%description |
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OpenNN is a software library written in C++ for predictive |
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analytics. It implements neural networks, the most successful |
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deep learning method. The main advantage of OpenNN is its high |
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performance. This library outstands in terms of execution speed |
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and memory allocation. It is constantly optimized and parallelized |
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in order to maximize its efficiency. Some typical applications of |
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OpenNN are function regression (modelling), pattern recognition |
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(classification) and time series prediction (forecasting). |
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%track |
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prog opennn:base = { |
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version = %{V_opkg} |
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url = https://github.com/Artelnics/opennn/releases |
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regex = v(__VER__)\.tar\.gz |
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} |
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prog opennn:snap = { |
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version = %{V_dist} |
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url = http://download.openpkg.org/components/versioned/opennn/ |
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regex = opennn-(__VER__)\.tar\.xz |
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} |
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%prep |
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%setup -q -n opennn |
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%build |
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mkdir build |
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cd build |
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cmake \ |
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-DCMAKE_BUILD_TYPE="Release" \ |
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-DCMAKE_INSTALL_PREFIX="%{l_prefix}" \ |
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-DCMAKE_C_COMPILER="%{l_cc}" \ |
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-DCMAKE_C_FLAGS="%{l_cflags} %{l_cppflags} -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-enum-compare" \ |
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-DCMAKE_EXE_LINKER_FLAGS="%{l_ldflags}" \ |
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-DCMAKE_CXX_COMPILER="%{l_cxx}" \ |
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-DCMAKE_CXX_FLAGS="%{l_cxxflags} -std=c++14 -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-enum-compare" \ |
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-DBUILD_SHARED_LIBS=OFF \ |
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-DOpenNN_BUILD_EXAMPLES=OFF \ |
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-DOpenNN_BUILD_BLANK=OFF \ |
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-DOpenNN_BUILD_TESTS=OFF \ |
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.. |
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%{l_make} %{l_mflags -O} |
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%install |
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%{l_shtool} mkdir -f -p -m 755 \ |
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$RPM_BUILD_ROOT%{l_prefix}/lib \ |
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$RPM_BUILD_ROOT%{l_prefix}/include/opennn |
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%{l_shtool} install -c -m 644 \ |
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build/opennn/libopennn.a $RPM_BUILD_ROOT%{l_prefix}/lib/ |
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%{l_shtool} install -c -m 644 \ |
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opennn/*.h $RPM_BUILD_ROOT%{l_prefix}/include/opennn/ |
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%{l_rpmtool} files -v -ofiles -r$RPM_BUILD_ROOT %{l_files_std} |
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%files -f files |
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%clean |
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