# Source code for abydos.distance._harris_lahey

# Copyright 2018-2020 by Christopher C. Little.
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"""abydos.distance._harris_lahey.

Harris & Lahey similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['HarrisLahey']

[docs]class HarrisLahey(_TokenDistance): r"""Harris & Lahey similarity. For two sets X and Y and a population N, Harris & Lahey similarity :cite:Harris:1978 is .. math:: sim_{HarrisLahey}(X, Y) = \frac{|X \cap Y|}{|X \cup Y|}\cdot \frac{|N \setminus Y| + |N \setminus X|}{2|N|}+ \frac{|(N \setminus X) \setminus Y|}{|N \setminus (X \cap Y)|}\cdot \frac{|X| + |Y|}{2|N|} In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n, this is .. math:: sim_{HarrisLahey} = \frac{a}{a+b+c}\cdot\frac{2d+b+c}{2n}+ \frac{d}{d+b+c}\cdot\frac{2a+b+c}{2n} Notes ----- Most catalogs of similarity coefficients :cite:Warrens:2008,Morris:2012,Xiang:2013 omit the :math:n terms in the denominators, but the worked example in :cite:Harris:1978 makes it clear that this is intended in the original. .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize HarrisLahey instance. Parameters ---------- alphabet : Counter, collection, int, or None This represents the alphabet of possible tokens. See :ref:alphabet <alphabet> description in :py:class:_TokenDistance for details. tokenizer : _Tokenizer A tokenizer instance from the :py:mod:abydos.tokenizer package intersection_type : str Specifies the intersection type, and set type as a result: See :ref:intersection_type <intersection_type> description in :py:class:_TokenDistance for details. **kwargs Arbitrary keyword arguments Other Parameters ---------------- qval : int The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value. metric : _Distance A string distance measure class for use in the soft and fuzzy variants. threshold : float A threshold value, similarities above which are counted as members of the intersection for the fuzzy variant. .. versionadded:: 0.4.0 """ super(HarrisLahey, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return the Harris & Lahey similarity of two strings. Parameters ---------- src : str Source string (or QGrams/Counter objects) for comparison tar : str Target string (or QGrams/Counter objects) for comparison Returns ------- float Harris & Lahey similarity Examples -------- >>> cmp = HarrisLahey() >>> cmp.sim('cat', 'hat') 0.3367085964820711 >>> cmp.sim('Niall', 'Neil') 0.22761577457069784 >>> cmp.sim('aluminum', 'Catalan') 0.07244410503054725 >>> cmp.sim('ATCG', 'TAGC') 0.006296204706372345 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 self._tokenize(src, tar) a = self._intersection_card() b = self._src_only_card() c = self._tar_only_card() d = self._total_complement_card() n = self._population_unique_card() score = 0.0 if a and (d + b + c): score += a / (a + b + c) * (2 * d + b + c) / (2 * n) if d and (a + b + c): score += d / (d + b + c) * (2 * a + b + c) / (2 * n) return score
if __name__ == '__main__': import doctest doctest.testmod()