%global _empty_manifest_terminate_build 0 Name: python-fastcluster Version: 1.2.6 Release: 1 Summary: Fast hierarchical clustering routines for R and Python. License: BSD URL: http://danifold.net Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5d/b8/f143d907d93bd4a3dd51d07c4e79b37bedbfc2177f4949bfa0d6ba0af647/fastcluster-1.2.6.tar.gz Requires: python3-numpy Requires: python3-scipy %description This library provides Python functions for hierarchical clustering. It generates hierarchical clusters from distance matrices or from vector data. This module is intended to replace the functions ``` linkage, single, complete, average, weighted, centroid, median, ward ``` in the module [`scipy.cluster.hierarchy`]( https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) with the same functionality but much faster algorithms. Moreover, the function `linkage_vector` provides memory-efficient clustering for vector data. The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/NumPy. The core implementation of this library is in C++ for efficiency. **User manual:** [fastcluster.pdf]( https://github.com/dmuellner/fastcluster/raw/master/docs/fastcluster.pdf). The “Yule” distance function changed in fastcluster version 1.2.0. This is following a [change in SciPy 1.6.3]( https://github.com/scipy/scipy/commit/3b22d1da98dc1b5f64bc944c21f398d4ba782bce). It is recommended to use fastcluster version 1.1.x together with SciPy versions before 1.6.3 and fastcluster 1.2.x with SciPy ≥1.6.3. The fastcluster package is considered stable and will undergo few changes from now on. If some years from now there have not been any updates, this does not necessarily mean that the package is unmaintained but maybe it just was not necessary to correct anything. Of course, please still report potential bugs and incompatibilities to daniel@danifold.net. You may also use [my GitHub repository](https://github.com/dmuellner/fastcluster/) for bug reports, pull requests etc. Note that [PyPI](https://pypi.org/project/fastcluster/) and [my GitHub repository](https://github.com/dmuellner/fastcluster/) host the source code for the Python interface only. The archive with both the R and the Python interface is available on [CRAN](https://CRAN.R-project.org/package=fastcluster) and the GitHub repository [“cran/fastcluster”](https://github.com/cran/fastcluster). Even though I appear as the author also of this second GitHub repository, this is just an automatic, read-only mirror of the CRAN archive, so please do not attempt to report bugs or contact me via this repository. Installation files for Windows are provided on [PyPI]( https://pypi.org/project/fastcluster/#files) and on [Christoph Gohlke's web page](http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster). Christoph Dalitz wrote a pure [C++ interface to fastcluster]( https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/). Reference: Daniel Müllner, *fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python*, Journal of Statistical Software, **53** (2013), no. 9, 1–18, https://doi.org/10.18637/jss.v053.i09. %package -n python3-fastcluster Summary: Fast hierarchical clustering routines for R and Python. Provides: python-fastcluster BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-fastcluster This library provides Python functions for hierarchical clustering. It generates hierarchical clusters from distance matrices or from vector data. This module is intended to replace the functions ``` linkage, single, complete, average, weighted, centroid, median, ward ``` in the module [`scipy.cluster.hierarchy`]( https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) with the same functionality but much faster algorithms. Moreover, the function `linkage_vector` provides memory-efficient clustering for vector data. The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/NumPy. The core implementation of this library is in C++ for efficiency. **User manual:** [fastcluster.pdf]( https://github.com/dmuellner/fastcluster/raw/master/docs/fastcluster.pdf). The “Yule” distance function changed in fastcluster version 1.2.0. This is following a [change in SciPy 1.6.3]( https://github.com/scipy/scipy/commit/3b22d1da98dc1b5f64bc944c21f398d4ba782bce). It is recommended to use fastcluster version 1.1.x together with SciPy versions before 1.6.3 and fastcluster 1.2.x with SciPy ≥1.6.3. The fastcluster package is considered stable and will undergo few changes from now on. If some years from now there have not been any updates, this does not necessarily mean that the package is unmaintained but maybe it just was not necessary to correct anything. Of course, please still report potential bugs and incompatibilities to daniel@danifold.net. You may also use [my GitHub repository](https://github.com/dmuellner/fastcluster/) for bug reports, pull requests etc. Note that [PyPI](https://pypi.org/project/fastcluster/) and [my GitHub repository](https://github.com/dmuellner/fastcluster/) host the source code for the Python interface only. The archive with both the R and the Python interface is available on [CRAN](https://CRAN.R-project.org/package=fastcluster) and the GitHub repository [“cran/fastcluster”](https://github.com/cran/fastcluster). Even though I appear as the author also of this second GitHub repository, this is just an automatic, read-only mirror of the CRAN archive, so please do not attempt to report bugs or contact me via this repository. Installation files for Windows are provided on [PyPI]( https://pypi.org/project/fastcluster/#files) and on [Christoph Gohlke's web page](http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster). Christoph Dalitz wrote a pure [C++ interface to fastcluster]( https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/). Reference: Daniel Müllner, *fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python*, Journal of Statistical Software, **53** (2013), no. 9, 1–18, https://doi.org/10.18637/jss.v053.i09. %package help Summary: Development documents and examples for fastcluster Provides: python3-fastcluster-doc %description help This library provides Python functions for hierarchical clustering. It generates hierarchical clusters from distance matrices or from vector data. This module is intended to replace the functions ``` linkage, single, complete, average, weighted, centroid, median, ward ``` in the module [`scipy.cluster.hierarchy`]( https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html) with the same functionality but much faster algorithms. Moreover, the function `linkage_vector` provides memory-efficient clustering for vector data. The interface is very similar to MATLAB's Statistics Toolbox API to make code easier to port from MATLAB to Python/NumPy. The core implementation of this library is in C++ for efficiency. **User manual:** [fastcluster.pdf]( https://github.com/dmuellner/fastcluster/raw/master/docs/fastcluster.pdf). The “Yule” distance function changed in fastcluster version 1.2.0. This is following a [change in SciPy 1.6.3]( https://github.com/scipy/scipy/commit/3b22d1da98dc1b5f64bc944c21f398d4ba782bce). It is recommended to use fastcluster version 1.1.x together with SciPy versions before 1.6.3 and fastcluster 1.2.x with SciPy ≥1.6.3. The fastcluster package is considered stable and will undergo few changes from now on. If some years from now there have not been any updates, this does not necessarily mean that the package is unmaintained but maybe it just was not necessary to correct anything. Of course, please still report potential bugs and incompatibilities to daniel@danifold.net. You may also use [my GitHub repository](https://github.com/dmuellner/fastcluster/) for bug reports, pull requests etc. Note that [PyPI](https://pypi.org/project/fastcluster/) and [my GitHub repository](https://github.com/dmuellner/fastcluster/) host the source code for the Python interface only. The archive with both the R and the Python interface is available on [CRAN](https://CRAN.R-project.org/package=fastcluster) and the GitHub repository [“cran/fastcluster”](https://github.com/cran/fastcluster). Even though I appear as the author also of this second GitHub repository, this is just an automatic, read-only mirror of the CRAN archive, so please do not attempt to report bugs or contact me via this repository. Installation files for Windows are provided on [PyPI]( https://pypi.org/project/fastcluster/#files) and on [Christoph Gohlke's web page](http://www.lfd.uci.edu/~gohlke/pythonlibs/#fastcluster). Christoph Dalitz wrote a pure [C++ interface to fastcluster]( https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/). Reference: Daniel Müllner, *fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python*, Journal of Statistical Software, **53** (2013), no. 9, 1–18, https://doi.org/10.18637/jss.v053.i09. %prep %autosetup -n fastcluster-1.2.6 %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-fastcluster -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 1.2.6-1 - Package Spec generated