%global _empty_manifest_terminate_build 0
Name:		python-momentum
Version:	0.2.7
Release:	1
Summary:	Running estimates of moments
License:	MIT
URL:		https://github.com/microprediction/momentum
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9d/7c/1f52beeb73b440ecd2cdccf1898e8dbc0e796af158a890e95ddfa585f49f/momentum-0.2.7.tar.gz
BuildArch:	noarch

Requires:	python3-wheel

%description
# momentum ![tests](https://github.com/microprediction/momentum/workflows/tests/badge.svg) ![deploy](https://github.com/microprediction/momentum/workflows/deploy/badge.svg)
A trivial mini-package for computing the running univariate mean, variance, kurtosis and skew

- No dependencies ... not even numpy.
- No classes ... unless you want them.
- State is a dict, for trivial serialization. 
- Tested against scipy, creme, statistics

For multivariate covariance updating, maybe see [precise](https://github.com/microprediction/precise). 

### Install 

    pip install momentum

### Usage: running mean, var

    from momentum import var_init, var_update
    from pprint import pprint
    
    m = var_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = var_update(m,x)
    pprint(m)
    
    

### Usage: running mean, var, kurtosis and skew 

    from momentum import kurtosis_init, kurtosis_update
    
    m = kurtosis_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = kurtosis_update(m,x)
    pprint(m)
    
    
File an issue if you need more help using this. 
    
  
### Usage: running recency-weighted mean, var

    from momentum import rvar_init, rvar_update
    from pprint import pprint
    
    m = rvar_init(rho=0.01,n=15)
    for x in [5,3,2.4,1.0,5.0]:
        m = rvar_update(m,x)
    pprint(m)
    
This will switch from running variance to a weighted variance after 15 data points. 
    




%package -n python3-momentum
Summary:	Running estimates of moments
Provides:	python-momentum
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-momentum
# momentum ![tests](https://github.com/microprediction/momentum/workflows/tests/badge.svg) ![deploy](https://github.com/microprediction/momentum/workflows/deploy/badge.svg)
A trivial mini-package for computing the running univariate mean, variance, kurtosis and skew

- No dependencies ... not even numpy.
- No classes ... unless you want them.
- State is a dict, for trivial serialization. 
- Tested against scipy, creme, statistics

For multivariate covariance updating, maybe see [precise](https://github.com/microprediction/precise). 

### Install 

    pip install momentum

### Usage: running mean, var

    from momentum import var_init, var_update
    from pprint import pprint
    
    m = var_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = var_update(m,x)
    pprint(m)
    
    

### Usage: running mean, var, kurtosis and skew 

    from momentum import kurtosis_init, kurtosis_update
    
    m = kurtosis_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = kurtosis_update(m,x)
    pprint(m)
    
    
File an issue if you need more help using this. 
    
  
### Usage: running recency-weighted mean, var

    from momentum import rvar_init, rvar_update
    from pprint import pprint
    
    m = rvar_init(rho=0.01,n=15)
    for x in [5,3,2.4,1.0,5.0]:
        m = rvar_update(m,x)
    pprint(m)
    
This will switch from running variance to a weighted variance after 15 data points. 
    




%package help
Summary:	Development documents and examples for momentum
Provides:	python3-momentum-doc
%description help
# momentum ![tests](https://github.com/microprediction/momentum/workflows/tests/badge.svg) ![deploy](https://github.com/microprediction/momentum/workflows/deploy/badge.svg)
A trivial mini-package for computing the running univariate mean, variance, kurtosis and skew

- No dependencies ... not even numpy.
- No classes ... unless you want them.
- State is a dict, for trivial serialization. 
- Tested against scipy, creme, statistics

For multivariate covariance updating, maybe see [precise](https://github.com/microprediction/precise). 

### Install 

    pip install momentum

### Usage: running mean, var

    from momentum import var_init, var_update
    from pprint import pprint
    
    m = var_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = var_update(m,x)
    pprint(m)
    
    

### Usage: running mean, var, kurtosis and skew 

    from momentum import kurtosis_init, kurtosis_update
    
    m = kurtosis_init()
    for x in [5,3,2.4,1.0,5.0]:
        m = kurtosis_update(m,x)
    pprint(m)
    
    
File an issue if you need more help using this. 
    
  
### Usage: running recency-weighted mean, var

    from momentum import rvar_init, rvar_update
    from pprint import pprint
    
    m = rvar_init(rho=0.01,n=15)
    for x in [5,3,2.4,1.0,5.0]:
        m = rvar_update(m,x)
    pprint(m)
    
This will switch from running variance to a weighted variance after 15 data points. 
    




%prep
%autosetup -n momentum-0.2.7

%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-momentum -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.7-1
- Package Spec generated