Source code for abydos.distance._mlipns

# Copyright 2014-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
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"""abydos.distance._mlipns.

The distance.hamming module implements Hamming and related distance functions.
"""

from typing import Any

from ._distance import _Distance
from ._hamming import Hamming

__all__ = ['MLIPNS']


[docs] class MLIPNS(_Distance): """MLIPNS similarity. Modified Language-Independent Product Name Search (MLIPNS) is described in :cite:`Shannaq:2010`. This function returns only 1.0 (similar) or 0.0 (not similar). LIPNS similarity is identical to normalized Hamming similarity. .. versionadded:: 0.3.6 """ _hamming = Hamming(diff_lens=True) def __init__( self, threshold: float = 0.25, max_mismatches: int = 2, **kwargs: Any ) -> None: """Initialize MLIPNS instance. Parameters ---------- threshold : float A number [0, 1] indicating the maximum similarity score, below which the strings are considered 'similar' (0.25 by default) max_mismatches : int A number indicating the allowable number of mismatches to remove before declaring two strings not similar (2 by default) **kwargs Arbitrary keyword arguments .. versionadded:: 0.4.0 """ super(MLIPNS, self).__init__(**kwargs) self._threshold = threshold self._max_mismatches = max_mismatches
[docs] def sim(self, src: str, tar: str) -> float: """Return the MLIPNS similarity of two strings. Parameters ---------- src : str Source string for comparison tar : str Target string for comparison Returns ------- float MLIPNS similarity Examples -------- >>> cmp = MLIPNS() >>> cmp.sim('cat', 'hat') 1.0 >>> cmp.sim('Niall', 'Neil') 0.0 >>> cmp.sim('aluminum', 'Catalan') 0.0 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.1.0 .. versionchanged:: 0.3.6 Encapsulated in class """ if tar == src: return 1.0 if not src or not tar: return 0.0 mismatches = 0 ham = self._hamming.dist_abs(src, tar) max_length = max(len(src), len(tar)) while src and tar and mismatches <= self._max_mismatches: if ( max_length < 1 or (1 - (max_length - ham) / max_length) <= self._threshold ): return 1.0 else: mismatches += 1 ham -= 1 max_length -= 1 if max_length < 1: return 1.0 return 0.0
if __name__ == '__main__': import doctest doctest.testmod()