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|
%global _empty_manifest_terminate_build 0
Name: python-dm-pybloom
Version: 3.0.4
Release: 1
Summary: Datamaran's fork of Pybloom adapted to Python3
License: MIT
URL: https://github.com/datamaranai/python-bloomfilter/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6e/27/2e1d03cc38f0cb89149a47702e97895d19ac4d29919c25526e7f7b26db12/dm_pybloom-3.0.4.tar.gz
BuildArch: noarch
Requires: python3-bitarray
%description
``dm_pybloom`` is a module that includes a Bloom Filter data structure along with
an implmentation of Scalable Bloom Filters as discussed in:
P. Almeida, C.Baquero, N. Preguiça, D. Hutchison, Scalable Bloom Filters,
(GLOBECOM 2007), IEEE, 2007.
Bloom filters are great if you understand what amount of bits you need to set
aside early to store your entire set. Scalable Bloom Filters allow your bloom
filter bits to grow as a function of false positive probability and size.
A filter is "full" when at capacity: M * ((ln 2 ^ 2) / abs(ln p)), where M
is the number of bits and p is the false positive probability. When capacity
is reached a new filter is then created exponentially larger than the last
with a tighter probability of false positives and a larger number of hash
functions.
>>> from dm_pybloom import BloomFilter
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> [f.add(x) for x in range(10)]
[False, False, False, False, False, False, False, False, False, False]
>>> all([(x in f) for x in range(10)])
True
>>> 10 in f
False
>>> 5 in f
True
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> for i in xrange(0, f.capacity):
>>> (1.0 - (len(f) / float(f.capacity))) <= f.error_rate + 2e-18
True
>>> from dm_pybloom import ScalableBloomFilter
>>> sbf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)
>>> count = 10000
>>> for i in xrange(0, count):
>>> (1.0 - (len(sbf) / float(count))) <= sbf.error_rate + 2e-18
True
# len(sbf) may not equal the entire input length. 0.01% error is well
# below the default 0.1% error threshold. As the capacity goes up, the
# error will approach 0.1%.
%package -n python3-dm-pybloom
Summary: Datamaran's fork of Pybloom adapted to Python3
Provides: python-dm-pybloom
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-dm-pybloom
``dm_pybloom`` is a module that includes a Bloom Filter data structure along with
an implmentation of Scalable Bloom Filters as discussed in:
P. Almeida, C.Baquero, N. Preguiça, D. Hutchison, Scalable Bloom Filters,
(GLOBECOM 2007), IEEE, 2007.
Bloom filters are great if you understand what amount of bits you need to set
aside early to store your entire set. Scalable Bloom Filters allow your bloom
filter bits to grow as a function of false positive probability and size.
A filter is "full" when at capacity: M * ((ln 2 ^ 2) / abs(ln p)), where M
is the number of bits and p is the false positive probability. When capacity
is reached a new filter is then created exponentially larger than the last
with a tighter probability of false positives and a larger number of hash
functions.
>>> from dm_pybloom import BloomFilter
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> [f.add(x) for x in range(10)]
[False, False, False, False, False, False, False, False, False, False]
>>> all([(x in f) for x in range(10)])
True
>>> 10 in f
False
>>> 5 in f
True
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> for i in xrange(0, f.capacity):
>>> (1.0 - (len(f) / float(f.capacity))) <= f.error_rate + 2e-18
True
>>> from dm_pybloom import ScalableBloomFilter
>>> sbf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)
>>> count = 10000
>>> for i in xrange(0, count):
>>> (1.0 - (len(sbf) / float(count))) <= sbf.error_rate + 2e-18
True
# len(sbf) may not equal the entire input length. 0.01% error is well
# below the default 0.1% error threshold. As the capacity goes up, the
# error will approach 0.1%.
%package help
Summary: Development documents and examples for dm-pybloom
Provides: python3-dm-pybloom-doc
%description help
``dm_pybloom`` is a module that includes a Bloom Filter data structure along with
an implmentation of Scalable Bloom Filters as discussed in:
P. Almeida, C.Baquero, N. Preguiça, D. Hutchison, Scalable Bloom Filters,
(GLOBECOM 2007), IEEE, 2007.
Bloom filters are great if you understand what amount of bits you need to set
aside early to store your entire set. Scalable Bloom Filters allow your bloom
filter bits to grow as a function of false positive probability and size.
A filter is "full" when at capacity: M * ((ln 2 ^ 2) / abs(ln p)), where M
is the number of bits and p is the false positive probability. When capacity
is reached a new filter is then created exponentially larger than the last
with a tighter probability of false positives and a larger number of hash
functions.
>>> from dm_pybloom import BloomFilter
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> [f.add(x) for x in range(10)]
[False, False, False, False, False, False, False, False, False, False]
>>> all([(x in f) for x in range(10)])
True
>>> 10 in f
False
>>> 5 in f
True
>>> f = BloomFilter(capacity=1000, error_rate=0.001)
>>> for i in xrange(0, f.capacity):
>>> (1.0 - (len(f) / float(f.capacity))) <= f.error_rate + 2e-18
True
>>> from dm_pybloom import ScalableBloomFilter
>>> sbf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)
>>> count = 10000
>>> for i in xrange(0, count):
>>> (1.0 - (len(sbf) / float(count))) <= sbf.error_rate + 2e-18
True
# len(sbf) may not equal the entire input length. 0.01% error is well
# below the default 0.1% error threshold. As the capacity goes up, the
# error will approach 0.1%.
%prep
%autosetup -n dm-pybloom-3.0.4
%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-dm-pybloom -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.4-1
- Package Spec generated
|
