Metadata-Version: 1.1
Name: dm_pybloom
Version: 3.0.4
Summary: Datamaran's fork of Pybloom adapted to Python3
Home-page: https://github.com/datamaranai/python-bloomfilter/
Author: jxub
Author-email: jjanarek@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: 
        dm_pybloom
        =======
        
        .. image:: https://travis-ci.org/jaybaird/python-bloomfilter.svg?branch=master
            :target: https://travis-ci.org/jaybaird/python-bloomfilter
        
        ``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.
        
        .. code-block:: python
        
            >>> 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):
            ...     _ = f.add(i)
            >>> (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):
            ...     _ = sbf.add(i)
            ...
            >>> (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%.
        
Keywords: data structures,bloom filter,bloom,filter,probabilistic,set
Platform: any
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
