%global _empty_manifest_terminate_build 0 Name: python-flann Version: 1.6.13 Release: 1 Summary: flann is the python 3.6 bindings for FLANN - Fast Library for Approximate Nearest Neighbors. License: BSD URL: https://github.com/pypa/flann Source0: https://mirrors.nju.edu.cn/pypi/web/packages/67/ba/04b7e7d0c84a7c9931cfaee088a2c70b8327538f76eae8b8d7f81f9748db/flann-1.6.13.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-check-manifest Requires: python3-coverage %description 1. Introduction ~~~~~~~~~~~~~~~ pyflann is the python bindings for `FLANN - Fast Library for Approximate Nearest Neighbors `__. 2. Install ~~~~~~~~~~ For python2 ^^^^^^^^^^^ This package uses distutils, which is the default way of installing python modules. To install in your home directory, securely run the following: git clone https://github.com/primetang/pyflann.git cd pyflann [sudo] python setup.py install Or directly through ``pip`` to install it: [sudo] pip install pyflann For python3 ^^^^^^^^^^^ Please refer to `this issuse `__ to modify the code. 3. Usage ~~~~~~~~ Use it just like the following code: from pyflann import * import numpy as np dataset = np.array( [[1., 1, 1, 2, 3], [10, 10, 10, 3, 2], [100, 100, 2, 30, 1] ]) testset = np.array( [[1., 1, 1, 1, 1], [90, 90, 10, 10, 1] ]) flann = FLANN() result, dists = flann.nn( dataset, testset, 2, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists dataset = np.random.rand(10000, 128) testset = np.random.rand(1000, 128) flann = FLANN() result, dists = flann.nn( dataset, testset, 5, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists %package -n python3-flann Summary: flann is the python 3.6 bindings for FLANN - Fast Library for Approximate Nearest Neighbors. Provides: python-flann BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-flann 1. Introduction ~~~~~~~~~~~~~~~ pyflann is the python bindings for `FLANN - Fast Library for Approximate Nearest Neighbors `__. 2. Install ~~~~~~~~~~ For python2 ^^^^^^^^^^^ This package uses distutils, which is the default way of installing python modules. To install in your home directory, securely run the following: git clone https://github.com/primetang/pyflann.git cd pyflann [sudo] python setup.py install Or directly through ``pip`` to install it: [sudo] pip install pyflann For python3 ^^^^^^^^^^^ Please refer to `this issuse `__ to modify the code. 3. Usage ~~~~~~~~ Use it just like the following code: from pyflann import * import numpy as np dataset = np.array( [[1., 1, 1, 2, 3], [10, 10, 10, 3, 2], [100, 100, 2, 30, 1] ]) testset = np.array( [[1., 1, 1, 1, 1], [90, 90, 10, 10, 1] ]) flann = FLANN() result, dists = flann.nn( dataset, testset, 2, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists dataset = np.random.rand(10000, 128) testset = np.random.rand(1000, 128) flann = FLANN() result, dists = flann.nn( dataset, testset, 5, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists %package help Summary: Development documents and examples for flann Provides: python3-flann-doc %description help 1. Introduction ~~~~~~~~~~~~~~~ pyflann is the python bindings for `FLANN - Fast Library for Approximate Nearest Neighbors `__. 2. Install ~~~~~~~~~~ For python2 ^^^^^^^^^^^ This package uses distutils, which is the default way of installing python modules. To install in your home directory, securely run the following: git clone https://github.com/primetang/pyflann.git cd pyflann [sudo] python setup.py install Or directly through ``pip`` to install it: [sudo] pip install pyflann For python3 ^^^^^^^^^^^ Please refer to `this issuse `__ to modify the code. 3. Usage ~~~~~~~~ Use it just like the following code: from pyflann import * import numpy as np dataset = np.array( [[1., 1, 1, 2, 3], [10, 10, 10, 3, 2], [100, 100, 2, 30, 1] ]) testset = np.array( [[1., 1, 1, 1, 1], [90, 90, 10, 10, 1] ]) flann = FLANN() result, dists = flann.nn( dataset, testset, 2, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists dataset = np.random.rand(10000, 128) testset = np.random.rand(1000, 128) flann = FLANN() result, dists = flann.nn( dataset, testset, 5, algorithm="kmeans", branching=32, iterations=7, checks=16) print result print dists %prep %autosetup -n flann-1.6.13 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-flann -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 1.6.13-1 - Package Spec generated