Metadata-Version: 1.1
Name: bloom_filter
Version: 1.3
Summary: Pure Python Bloom Filter module
Home-page: https://github.com/hiway/python-bloom-filter
Author: Harshad Sharma
Author-email: harshad@sharma.io
License: MIT
Description: 
        A pure python bloom filter (low storage requirement, probabilistic
        set datastructure) is provided.  It is known to work on CPython 2.x,
        CPython 3.x, Pypy and Jython.
        
        Includes mmap, in-memory and disk-seek backends.
        
        The user specifies the desired maximum number of elements and the
        desired maximum false positive probability, and the module
        calculates the rest.
        
        Usage:
        
        ::
        
            from bloom_filter import BloomFilter
        
            # instantiate BloomFilter with custom settings,
            # max_elements is how many elements you expect the filter to hold.
            # error_rate defines accuracy; You can use defaults with
            # `BloomFilter()` without any arguments. Following example
            # is same as defaults:
            bloom = BloomFilter(max_elements=10000, error_rate=0.1)
        
            # Test whether the bloom-filter has seen a key:
            assert "test-key" in bloom is False
        
            # Mark the key as seen
            bloom.add("test-key")
        
            # Now check again
            assert "test-key" in bloom is True
            
        
Keywords: probabilistic set datastructure
Platform: Cross platform
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
