Source code for abydos.distance._kuhns_xi

# Copyright 2019-2020 by Christopher C. Little.
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"""abydos.distance._kuhns_xi.

Kuhns XI correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['KuhnsXI']


[docs]class KuhnsXI(_TokenDistance): r"""Kuhns XI correlation. For two sets X and Y and a population N, Kuhns XI correlation :cite:`Kuhns:1965`, the excess of Yule's Y over its independence value (Y), is .. math:: corr_{KuhnsXI}(X, Y) = \frac{|N| \cdot \delta(X, Y)}{(\sqrt{|X \cap Y| \cdot |(N \setminus X) \setminus Y|} + \sqrt{|X \setminus Y| \cdot |Y \setminus X|})^2} where .. math:: \delta(X, Y) = |X \cap Y| - \frac{|X| \cdot |Y|}{|N|} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: corr_{KuhnsXI} = \frac{n \cdot \delta(a+b, a+c)}{(\sqrt{ad}+\sqrt{bc})^2} where .. math:: \delta(a+b, a+c) = a - \frac{(a+b)(a+c)}{n} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize KuhnsXI 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(KuhnsXI, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Kuhns XI 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 Kuhns XI correlation Examples -------- >>> cmp = KuhnsXI() >>> cmp.corr('cat', 'hat') 0.9034892632818761 >>> cmp.corr('Niall', 'Neil') 0.8382551144735259 >>> cmp.corr('aluminum', 'Catalan') 0.5749826820237787 >>> cmp.corr('ATCG', 'TAGC') -1.0 .. 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() apbmapc = (a + b) * (a + c) if not apbmapc: delta_ab = a else: delta_ab = a - apbmapc / n if not delta_ab: return 0.0 else: # clamp to [-1.0, 1.0], strictly due to floating point precision # issues return max( -1.0, min( 1.0, (n * delta_ab) / max(1.0, ((a * d) ** 0.5 + (b * c) ** 0.5) ** 2), ), )
[docs] def sim(self, src, tar): """Return the Kuhns XI 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 Kuhns XI similarity Examples -------- >>> cmp = KuhnsXI() >>> cmp.sim('cat', 'hat') 0.951744631640938 >>> cmp.sim('Niall', 'Neil') 0.919127557236763 >>> cmp.sim('aluminum', 'Catalan') 0.7874913410118893 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__': import doctest doctest.testmod()