automatic import of python-pyclustertendopeneuler20.03

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+/pyclustertend-1.8.2.tar.gz
diff --git a/python-pyclustertend.spec b/python-pyclustertend.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pyclustertend
+Version:	1.8.2
+Release:	1
+Summary:	A package to assess cluster tendency for unsupervised learning
+License:	BSD-3-Clause
+URL:		https://github.com/lachhebo/pyclustertend
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/04/a5/0d0043f93d9d499c720866e9eba068da9d76ca9d519a22fd008913abf74e/pyclustertend-1.8.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-scikit-learn
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-numba
+
+%description
+# pyclustertend
+
+
+
+
+
+
+[![Build Status](https://travis-ci.com/lachhebo/pyclustertend.svg?branch=master)](https://travis-ci.com/lachhebo/pyclustertend)  [![PyPi Status](https://img.shields.io/pypi/v/pyclustertend.svg?color=brightgreen)](https://pypi.org/project/pyclustertend/) [![Documentation Status](https://readthedocs.org/projects/pyclustertend/badge/?version=master)](https://pyclustertend.readthedocs.io/en/master/) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![codecov](https://codecov.io/gh/lachhebo/pyclustertend/branch/master/graph/badge.svg)](https://codecov.io/gh/lachhebo/pyclustertend)
+[![DOI](https://zenodo.org/badge/187477036.svg)](https://zenodo.org/badge/latestdoi/187477036)
+
+pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
+
+Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :
+
+
+
+- [x] Hopkins Statistics
+- [x] VAT
+- [x] iVAT
+
+- [x] Metric based method (silhouette, calinksi, davies bouldin)
+
+## Installation
+
+```shell
+    pip install pyclustertend
+```
+
+## Usage
+
+### Example Hopkins
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import hopkins
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>hopkins(X,150)
+    0.18950453452838564
+```
+
+### Example VAT
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import vat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>vat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png" />
+
+### Example iVat
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import ivat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>ivat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png" />
+
+## Notes
+
+It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms
+do not really fit to massive databases. A first solution is to sample the data before using those algorithms. 
+
+
+%package -n python3-pyclustertend
+Summary:	A package to assess cluster tendency for unsupervised learning
+Provides:	python-pyclustertend
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pyclustertend
+# pyclustertend
+
+
+
+
+
+
+[![Build Status](https://travis-ci.com/lachhebo/pyclustertend.svg?branch=master)](https://travis-ci.com/lachhebo/pyclustertend)  [![PyPi Status](https://img.shields.io/pypi/v/pyclustertend.svg?color=brightgreen)](https://pypi.org/project/pyclustertend/) [![Documentation Status](https://readthedocs.org/projects/pyclustertend/badge/?version=master)](https://pyclustertend.readthedocs.io/en/master/) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![codecov](https://codecov.io/gh/lachhebo/pyclustertend/branch/master/graph/badge.svg)](https://codecov.io/gh/lachhebo/pyclustertend)
+[![DOI](https://zenodo.org/badge/187477036.svg)](https://zenodo.org/badge/latestdoi/187477036)
+
+pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
+
+Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :
+
+
+
+- [x] Hopkins Statistics
+- [x] VAT
+- [x] iVAT
+
+- [x] Metric based method (silhouette, calinksi, davies bouldin)
+
+## Installation
+
+```shell
+    pip install pyclustertend
+```
+
+## Usage
+
+### Example Hopkins
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import hopkins
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>hopkins(X,150)
+    0.18950453452838564
+```
+
+### Example VAT
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import vat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>vat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png" />
+
+### Example iVat
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import ivat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>ivat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png" />
+
+## Notes
+
+It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms
+do not really fit to massive databases. A first solution is to sample the data before using those algorithms. 
+
+
+%package help
+Summary:	Development documents and examples for pyclustertend
+Provides:	python3-pyclustertend-doc
+%description help
+# pyclustertend
+
+
+
+
+
+
+[![Build Status](https://travis-ci.com/lachhebo/pyclustertend.svg?branch=master)](https://travis-ci.com/lachhebo/pyclustertend)  [![PyPi Status](https://img.shields.io/pypi/v/pyclustertend.svg?color=brightgreen)](https://pypi.org/project/pyclustertend/) [![Documentation Status](https://readthedocs.org/projects/pyclustertend/badge/?version=master)](https://pyclustertend.readthedocs.io/en/master/) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![codecov](https://codecov.io/gh/lachhebo/pyclustertend/branch/master/graph/badge.svg)](https://codecov.io/gh/lachhebo/pyclustertend)
+[![DOI](https://zenodo.org/badge/187477036.svg)](https://zenodo.org/badge/latestdoi/187477036)
+
+pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
+
+Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :
+
+
+
+- [x] Hopkins Statistics
+- [x] VAT
+- [x] iVAT
+
+- [x] Metric based method (silhouette, calinksi, davies bouldin)
+
+## Installation
+
+```shell
+    pip install pyclustertend
+```
+
+## Usage
+
+### Example Hopkins
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import hopkins
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>hopkins(X,150)
+    0.18950453452838564
+```
+
+### Example VAT
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import vat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>vat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png" />
+
+### Example iVat
+
+```python
+    >>>from sklearn import datasets
+    >>>from pyclustertend import ivat
+    >>>from sklearn.preprocessing import scale
+    >>>X = scale(datasets.load_iris().data)
+    >>>ivat(X)
+```
+
+<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png" />
+
+## Notes
+
+It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms
+do not really fit to massive databases. A first solution is to sample the data before using those algorithms. 
+
+
+%prep
+%autosetup -n pyclustertend-1.8.2
+
+%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-pyclustertend -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.8.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4763c4c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d6fd5a693c8a5be8d99444cf63c39d9c  pyclustertend-1.8.2.tar.gz