%global _empty_manifest_terminate_build 0
Name:		python-fuzzy-c-means
Version:	1.7.0
Release:	1
Summary:	A simple python implementation of Fuzzy C-means algorithm.
License:	MIT
URL:		https://github.com/omadson/fuzzy-c-means
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9e/a1/fe18a9cb3a65fe0fbe2e96ec6acf0753a09fe4d24868f42530f7db5df22d/fuzzy_c_means-1.7.0.tar.gz
BuildArch:	noarch

Requires:	python3-joblib
Requires:	python3-numpy
Requires:	python3-pydantic
Requires:	python3-tabulate
Requires:	python3-tqdm
Requires:	python3-typer

%description
# fuzzy-c-means

![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)
[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)
[![Documentation Status](https://readthedocs.org/projects/fuzzy-c-means/badge/?version=latest)](https://fuzzy-c-means.readthedocs.io/en/latest/?badge=latest)
[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)
[![Downloads](https://pepy.tech/badge/fuzzy-c-means)](https://pepy.tech/project/fuzzy-c-means)
[![DOI](https://zenodo.org/badge/186457481.svg)](https://zenodo.org/badge/latestdoi/186457481)


**[Documentation](https://fuzzy-c-means.readthedocs.io/)** | **[Changelog](https://fuzzy-c-means.readthedocs.io/en/latest/CHANGELOG/)** | **[Citation](https://fuzzy-c-means.readthedocs.io/en/latest/citation/)**



`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.

## installation
the `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:
```
pip install fuzzy-c-means
```
## citation
if you use `fuzzy-c-means` package in your paper, please cite it in your publication.
```
@software{dias2019fuzzy,
  author       = {Madson Luiz Dantas Dias},
  title        = {fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.},
  month        = may,
  year         = 2019,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3066222},
  url          = {https://git.io/fuzzy-c-means}
}
```

<!-- ### citations
 - [Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH](https://doi.org/10.1177/1177932220909851)
 - [Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means](https://doi.org/10.24843/MITE.2020.v19i01.P14)
 - [Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment](https://doi.org/10.5220/0010176201050112)
 - [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://arxiv.org/abs/1910.07763)
 - [Fuzzy Clustering: an Application to Distributional Reinforcement Learning](https://doi.org/10.34726/hss.2021.86783)
 - [Fuzzy Clustering with Similarity Queries](https://arxiv.org/pdf/2106.02212.pdf)
 - [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://arxiv.org/abs/2110.09622)
 - [Unsupervised clustering-based spectral analysis of bio-dyed textile samples](http://urn.fi/urn:nbn:fi:uef-20211291) -->


## contributing and support

this project is open for contributions. here are some of the ways for you to contribute:
 - bug reports/fix
 - features requests
 - use-case demonstrations

please open an [issue](https://github.com/omadson/fuzzy-c-means/issues) with enough information for us to reproduce your problem. A [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) would be very helpful.

to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!

<!-- ## contributors
 - [Madson Dias](https://github.com/omadson)
 - [Dirk Nachbar](https://github.com/dirknbr)
 - [Alberth Florêncio](https://github.com/zealberth) -->

[1]: https://doi.org/10.1016/0098-3004(84)90020-7
[2]: http://scikit-learn.org/



%package -n python3-fuzzy-c-means
Summary:	A simple python implementation of Fuzzy C-means algorithm.
Provides:	python-fuzzy-c-means
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-fuzzy-c-means
# fuzzy-c-means

![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)
[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)
[![Documentation Status](https://readthedocs.org/projects/fuzzy-c-means/badge/?version=latest)](https://fuzzy-c-means.readthedocs.io/en/latest/?badge=latest)
[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)
[![Downloads](https://pepy.tech/badge/fuzzy-c-means)](https://pepy.tech/project/fuzzy-c-means)
[![DOI](https://zenodo.org/badge/186457481.svg)](https://zenodo.org/badge/latestdoi/186457481)


**[Documentation](https://fuzzy-c-means.readthedocs.io/)** | **[Changelog](https://fuzzy-c-means.readthedocs.io/en/latest/CHANGELOG/)** | **[Citation](https://fuzzy-c-means.readthedocs.io/en/latest/citation/)**



`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.

## installation
the `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:
```
pip install fuzzy-c-means
```
## citation
if you use `fuzzy-c-means` package in your paper, please cite it in your publication.
```
@software{dias2019fuzzy,
  author       = {Madson Luiz Dantas Dias},
  title        = {fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.},
  month        = may,
  year         = 2019,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3066222},
  url          = {https://git.io/fuzzy-c-means}
}
```

<!-- ### citations
 - [Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH](https://doi.org/10.1177/1177932220909851)
 - [Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means](https://doi.org/10.24843/MITE.2020.v19i01.P14)
 - [Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment](https://doi.org/10.5220/0010176201050112)
 - [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://arxiv.org/abs/1910.07763)
 - [Fuzzy Clustering: an Application to Distributional Reinforcement Learning](https://doi.org/10.34726/hss.2021.86783)
 - [Fuzzy Clustering with Similarity Queries](https://arxiv.org/pdf/2106.02212.pdf)
 - [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://arxiv.org/abs/2110.09622)
 - [Unsupervised clustering-based spectral analysis of bio-dyed textile samples](http://urn.fi/urn:nbn:fi:uef-20211291) -->


## contributing and support

this project is open for contributions. here are some of the ways for you to contribute:
 - bug reports/fix
 - features requests
 - use-case demonstrations

please open an [issue](https://github.com/omadson/fuzzy-c-means/issues) with enough information for us to reproduce your problem. A [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) would be very helpful.

to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!

<!-- ## contributors
 - [Madson Dias](https://github.com/omadson)
 - [Dirk Nachbar](https://github.com/dirknbr)
 - [Alberth Florêncio](https://github.com/zealberth) -->

[1]: https://doi.org/10.1016/0098-3004(84)90020-7
[2]: http://scikit-learn.org/



%package help
Summary:	Development documents and examples for fuzzy-c-means
Provides:	python3-fuzzy-c-means-doc
%description help
# fuzzy-c-means

![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)
[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)
[![Documentation Status](https://readthedocs.org/projects/fuzzy-c-means/badge/?version=latest)](https://fuzzy-c-means.readthedocs.io/en/latest/?badge=latest)
[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)
[![Downloads](https://pepy.tech/badge/fuzzy-c-means)](https://pepy.tech/project/fuzzy-c-means)
[![DOI](https://zenodo.org/badge/186457481.svg)](https://zenodo.org/badge/latestdoi/186457481)


**[Documentation](https://fuzzy-c-means.readthedocs.io/)** | **[Changelog](https://fuzzy-c-means.readthedocs.io/en/latest/CHANGELOG/)** | **[Citation](https://fuzzy-c-means.readthedocs.io/en/latest/citation/)**



`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.

## installation
the `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:
```
pip install fuzzy-c-means
```
## citation
if you use `fuzzy-c-means` package in your paper, please cite it in your publication.
```
@software{dias2019fuzzy,
  author       = {Madson Luiz Dantas Dias},
  title        = {fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.},
  month        = may,
  year         = 2019,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3066222},
  url          = {https://git.io/fuzzy-c-means}
}
```

<!-- ### citations
 - [Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH](https://doi.org/10.1177/1177932220909851)
 - [Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means](https://doi.org/10.24843/MITE.2020.v19i01.P14)
 - [Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment](https://doi.org/10.5220/0010176201050112)
 - [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://arxiv.org/abs/1910.07763)
 - [Fuzzy Clustering: an Application to Distributional Reinforcement Learning](https://doi.org/10.34726/hss.2021.86783)
 - [Fuzzy Clustering with Similarity Queries](https://arxiv.org/pdf/2106.02212.pdf)
 - [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://arxiv.org/abs/2110.09622)
 - [Unsupervised clustering-based spectral analysis of bio-dyed textile samples](http://urn.fi/urn:nbn:fi:uef-20211291) -->


## contributing and support

this project is open for contributions. here are some of the ways for you to contribute:
 - bug reports/fix
 - features requests
 - use-case demonstrations

please open an [issue](https://github.com/omadson/fuzzy-c-means/issues) with enough information for us to reproduce your problem. A [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) would be very helpful.

to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!

<!-- ## contributors
 - [Madson Dias](https://github.com/omadson)
 - [Dirk Nachbar](https://github.com/dirknbr)
 - [Alberth Florêncio](https://github.com/zealberth) -->

[1]: https://doi.org/10.1016/0098-3004(84)90020-7
[2]: http://scikit-learn.org/



%prep
%autosetup -n fuzzy-c-means-1.7.0

%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-fuzzy-c-means -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1
- Package Spec generated