# Source code for abydos.distance._benini_i

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

Benini I correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['BeniniI']

[docs]class BeniniI(_TokenDistance): r"""BeniniI correlation. For two sets X and Y and a population N, Benini I correlation, Benini's Index of Attraction, :cite:Benini:1901 is .. math:: corr_{BeniniI}(X, Y) = \frac{|X \cap Y| \cdot |(N \setminus X) \setminus Y| - |X \setminus Y| \cdot |Y \setminus X|}{|Y| \cdot |N \setminus X|} In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n, this is .. math:: corr_{BeniniI} = \frac{ad-bc}{(a+c)(c+d)} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize BeniniI 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(BeniniI, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Benini I correlation 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 Benini I correlation Examples -------- >>> cmp = BeniniI() >>> cmp.corr('cat', 'hat') 0.49743589743589745 >>> cmp.corr('Niall', 'Neil') 0.3953727506426735 >>> cmp.corr('aluminum', 'Catalan') 0.11485180412371133 >>> cmp.corr('ATCG', 'TAGC') -0.006418485237483954 .. 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() num = a * d - b * c if num == 0.0: return 0.0 return num / ((a + c) * (c + d))
[docs] def sim(self, src, tar): """Return the Benini I 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 Benini I similarity Examples -------- >>> cmp = BeniniI() >>> cmp.sim('cat', 'hat') 0.7487179487179487 >>> cmp.sim('Niall', 'Neil') 0.6976863753213367 >>> cmp.sim('aluminum', 'Catalan') 0.5574259020618557 >>> cmp.sim('ATCG', 'TAGC') 0.496790757381258 .. versionadded:: 0.4.0 """ return (1 + self.corr(src, tar)) / 2
if __name__ == '__main__': import doctest doctest.testmod()