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%global _empty_manifest_terminate_build 0
Name: python-unidip
Version: 0.1.1
Release: 1
Summary: Python port of the UniDip clustering algorithm
License: GPL
URL: http://github.com/BenjaminDoran/unidip
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1c/22/e2b39fd524297ecc6c439748c4e20d56a97a8829f2b2b1897365a0f23a19/unidip-0.1.1.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-matplotlib
%description
# UniDip Python Port
See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise
UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality.
## Install
coming soon...
```
pip3.6 install unidip
```
## Examples
### Basic Usage
```python
from unidip import UniDip
# create bi-modal distribution
dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3])
# sort data so returned indices are meaningful
dat = np.msort(dat)
# get start and stop indices of peaks
intervals = UniDip(dat).run()
```
### Advanced Options
* **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise.
* **mrg_dst**: Defines how close intervals must be before they are merged.
* **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer.
```python
intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run()
```
%package -n python3-unidip
Summary: Python port of the UniDip clustering algorithm
Provides: python-unidip
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-unidip
# UniDip Python Port
See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise
UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality.
## Install
coming soon...
```
pip3.6 install unidip
```
## Examples
### Basic Usage
```python
from unidip import UniDip
# create bi-modal distribution
dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3])
# sort data so returned indices are meaningful
dat = np.msort(dat)
# get start and stop indices of peaks
intervals = UniDip(dat).run()
```
### Advanced Options
* **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise.
* **mrg_dst**: Defines how close intervals must be before they are merged.
* **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer.
```python
intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run()
```
%package help
Summary: Development documents and examples for unidip
Provides: python3-unidip-doc
%description help
# UniDip Python Port
See reference paper: http://www.kdd.org/kdd2016/subtopic/view/skinny-dip-clustering-in-a-sea-of-noise
UniDip is a noise robust clustering algorithm for 1 dimensional numeric data. It recursively extracts peaks of density in the data utilizing the Hartigan Dip-test of Unimodality.
## Install
coming soon...
```
pip3.6 install unidip
```
## Examples
### Basic Usage
```python
from unidip import UniDip
# create bi-modal distribution
dat = np.concatenate([np.random.randn(200)-3, np.random.randn(200)+3])
# sort data so returned indices are meaningful
dat = np.msort(dat)
# get start and stop indices of peaks
intervals = UniDip(dat).run()
```
### Advanced Options
* **alpha**: control sensitivity as p-value. Default is 0.05. increase to isolate more peaks with less confidence. Or, decrease to isolate only peaks that are least likely to be noise.
* **mrg_dst**: Defines how close intervals must be before they are merged.
* **ntrials**: how many trials are run in Hartigan Dip Test more trials adds confidance but takes longer.
```python
intervals = UniDip(dat, alpha=0.001, ntrials=1000, mrg_dst=5).run()
```
%prep
%autosetup -n unidip-0.1.1
%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-unidip -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1-1
- Package Spec generated
|
