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        "author": "Chuancong Gao",
        "author_email": "chuancong@gmail.com",
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        "description": "[![PyPI version](https://img.shields.io/pypi/v/TopSim.svg)](https://pypi.python.org/pypi/TopSim/)\n[![PyPI pyversions](https://img.shields.io/pypi/pyversions/TopSim.svg)](https://pypi.python.org/pypi/TopSim/)\n[![PyPI license](https://img.shields.io/pypi/l/TopSim.svg)](https://pypi.python.org/pypi/TopSim/)\n\nSearch the most similar strings against the query in Python 3. State-of-the-art algorithm and data structure are adopted for best efficiency. For both flexibility and efficiency, only set-based similarities are supported right now, including [Jaccard](https://en.wikipedia.org/wiki/Jaccard_index) and [Tversky](https://en.wikipedia.org/wiki/Tversky_index).\n\n# Installation\n\nThis package is available on PyPI. Just use `pip3 install -U TopSim` to install it.\n\n# CLI Usage\n\nYou can simply use the algorithm on terminal.\n\n```\nUsage:\n    topsim-cli <query> [options] [<file>]\n\n\nOptions:\n    -I                     Case-sensitive matching.\n    -k <k>                 Maximum number of search results. [default: 1]\n    --tie                  Include all the results with the same similarity of the \"k\"-th result. May return more than \"k\" results.\n\n    -s, --search           Search the query within each line rather than against the whole line, by preferring partial matching of the line.\n                           Tversky similarity is used instead of Jaccard similarity.\n    -e <e>                 Parameter for \"tversky\" similarity. [default: 0.001]\n\n    --mapping=<mapping>    Map each string to a set of either \"gram\"s or \"word\"s. [default: gram]\n    --numgrams=<numgrams>  Number of characters for each gram when mapping by \"gram\". [default: 2]\n\n    --quiet                Do not print additional information to standard error.\n```\n\n* The query is matched against each line of the input file (or standard input).\n\n- Each line and its similarity are separated by tab character `\\t`.\n\n# API Usage\n\nAlternatively, you can use the algorithm via API.\n\n``` python\nfrom topsim import TopSim\n\nts = TopSim([\n    \"python2\",\n    \"python2.7\",\n    \"python3\",\n    \"python3.6\",\n])\n\nprint(ts.search(\"python\", k=3)) # Return each similarity and the respective line numbers.\n```\n\n* Please check `topsim.py` for more optional parameters, like similarity function, etc.\n\n# Examples\n\n* Search the most similar line.\n\n`ls /usr/bin | topsim-cli \"top\"`\n\n```\ntop\t1.0\n```\n\n* Search the three most similar lines.\n\n`ls /usr/bin | topsim-cli \"top\" -k 3`\n\n```\ntop\t1.0\ntops\t0.5\niotop\t0.4286\n```\n\n* Use Jaccard similarity in default, which puts same weight on matching both the query and the lines.\n\n`ls /usr/bin | topsim-cli \"git\" -k 5`\n\n```\ngit\t1.0\nwait\t0.2857\ngit-shell\t0.2727\npluginkit\t0.2727\nkinit\t0.25\n```\n\n* Use Tversky similarity, which puts most weight on matching the query. Ideal when searching within long lines.\n\n`ls /usr/bin | topsim-cli \"git\" -k 5 -s`\n\n```\ngit\t1.0\ngit-shell\t0.7489\npluginkit\t0.7489\ngit-cvsserver\t0.7481\ngit-upload-pack\t0.7478\n```\n\n- For `n`-gram mapping, higher number of `n` for can result in better accuracy but fewer matches.\n\n`ls /usr/bin | topsim-cli \"git\" -k 5 -s --numgrams=3`\n\n```\ngit\t1.0\ngit-shell\t0.5993\ngit-cvsserver\t0.5988\ngit-upload-pack\t0.5986\ngit-receive-pack\t0.5984\n```\n\n- Full support of Chinese/Japanese/Korean.\n\n`cat test`\n\n``` text\n\u5730\u4e09\u9c9c\n\u7ea2\u70e7\u8089\n\u70e4\u5168\u725b\n\u6728\u987b\u8089\n\u571f\u8c46\u7096\u725b\u8089\n```\n\n`cat test | topsim-cli \"\u725b\u8089\" -k 3 -s`\n\n``` text\n\u571f\u8c46\u7096\u725b\u8089\t0.666\n\u7ea2\u70e7\u8089\t0.3332\n\u6728\u987b\u8089\t0.3332\n```\n\n# Tip\nI strongly encourage using PyPy instead of CPython to run the script for best performance.",
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