# Source code for abydos.distance._andres_marzo_delta

# Copyright 2019-2020 by Christopher C. Little.
# This file is part of Abydos.
#
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"""abydos.distance._andres_marzo_delta.

Andres & Marzo's Delta correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['AndresMarzoDelta']

[docs]class AndresMarzoDelta(_TokenDistance): r"""Andres & Marzo's Delta correlation. For two sets X and Y and a population N, Andres & Marzo's :math:\Delta correlation :cite:Andres:2004 is .. math:: corr_{AndresMarzo_\Delta}(X, Y) = \Delta = \frac{|X \cap Y| + |(N \setminus X) \setminus Y| - 2\sqrt{|X \setminus Y| \cdot |Y \setminus X|}}{|N|} In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n, this is .. math:: corr_{AndresMarzo_\Delta} = \Delta = \frac{a+d-2\sqrt{b \cdot c}}{n} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize AndresMarzoDelta 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(AndresMarzoDelta, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Andres & Marzo's Delta 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 Andres & Marzo's Delta correlation Examples -------- >>> cmp = AndresMarzoDelta() >>> cmp.corr('cat', 'hat') 0.9897959183673469 >>> cmp.corr('Niall', 'Neil') 0.9822344346552608 >>> cmp.corr('aluminum', 'Catalan') 0.9618259496215341 >>> cmp.corr('ATCG', 'TAGC') 0.9744897959183674 .. 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() num = a + d - 2 * (b * c) ** 0.5 if num == 0.0: return 0.0 return num / n
[docs] def sim(self, src, tar): """Return the Andres & Marzo's Delta 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 Andres & Marzo's Delta similarity Examples -------- >>> cmp = AndresMarzoDelta() >>> cmp.sim('cat', 'hat') 0.9948979591836735 >>> cmp.sim('Niall', 'Neil') 0.9911172173276304 >>> cmp.sim('aluminum', 'Catalan') 0.980912974810767 >>> cmp.sim('ATCG', 'TAGC') 0.9872448979591837 .. versionadded:: 0.4.0 """ return (self.corr(src, tar) + 1) / 2
if __name__ == '__main__': import doctest doctest.testmod()