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        "author": "Sambhav Jain",
        "author_email": "sambhavjain2612@gmail.com",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "# Bloom Filter\n\nImplemented in Python 3.\n\n- The price we pay for efficiency through bloom filters is that it is probabilistic in nature that means, there might be some **_False Positive_** results. False positive means, it might tell that given username is already taken but actually it\u00e2\u20ac\u2122s not.\n- Not being **_False Negative_** such that telling that username doesn't exist while it is there, i.e., if exists it reports it's existenece in terms of maybe, else if not present it is 100% confident to report the same.\n- Deleting elements from filter is not possible because, if we delete a single element by clearing bits at indices generated by k hash functions, it might cause deletion of few other elements.\n\n## Installation\n\n**`pip install bloomf==0.2`**\n\nDistributed as a [PyPi](https://pypi.org/project/bloomf/) Package.\n\n## Usage\n\nYou can use this bloom filter as follows -\n\n```python\nfrom bloomf import BloomFilter\n\nn = 10  # number of items to be added\np = 0.04  # FP Probablity\n\nfilter = BloomFilter(n, p)\n\nprint(\"Size of bit array: {}\" . format(filter.size))\nprint(\"False positive Probability: {}\" . format(filter.fp_prob))\nprint(\"Number of hash functions: {}\" . format(filter.hash_count))\n\nword_present = ['abound', 'abounds', 'abundance', 'abundant', 'accessable', 'bloom', 'blossom', 'bolster', 'bonny', 'bonus', 'bonuses']\nword_absent = ['bluff', 'cheater', 'hate', 'war', 'humanity', 'racism', 'hurt', 'facebook', 'sambhav', 'twitter']\n\nfor i in word_present:\n    filter.add(i)\n\ntest_words = word_present[:5] + word_absent\n\nfor word in test_words:\n    if filter.check(word):\n        if word in word_absent:\n            print(\"'{}' is a false positive!\" . format(word))\n        else:\n            print(\"'{}' is a probably present!\" . format(word))\n    else:\n        print(\"'{}' is 100% not present!\" . format(word))\n```\n\n### Dependencies\n\n- bitarray\n- mmh3\n\n\n",
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        "summary": "Simple Bloom Filter implmentation in Python",
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