Source code for abydos.distance._sokal_sneath_iv

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

Sokal & Sneath IV similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['SokalSneathIV']


[docs]class SokalSneathIV(_TokenDistance): r"""Sokal & Sneath IV similarity. For two sets X and Y and a population N, Sokal & Sneath IV similarity :cite:`Sokal:1963` is .. math:: sim_{SokalSneathIV}(X, Y) = \frac{1}{4}\Bigg( \frac{|X \cap Y|}{|X|}+ \frac{|X \cap Y|}{|Y|}+ \frac{|(N \setminus X) \setminus Y|} {|N \setminus Y|}+ \frac{|(N \setminus X) \setminus Y|} {|N \setminus X|} \Bigg) This is the fourth of five "Unnamed coefficients" presented in :cite:`Sokal:1963`. It corresponds to the first "Marginal totals in the Denominator" with "Negative Matches in Numerator Included". "Negative Matches in Numerator Excluded" corresponds to the Kulczynski II similarity, :class:`.KulczynskiII`. This is also Rogot & Goldberg's "adjusted agreement" :math:`A_1` :cite:`Rogot:1966`. In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{SokalSneathIV} = \frac{1}{4}\Big(\frac{a}{a+b}+\frac{a}{a+c}+ \frac{d}{b+d}+\frac{d}{c+d}\Big) .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize SokalSneathIV 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(SokalSneathIV, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return the Sokal & Sneath IV 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 Sokal & Sneath IV similarity Examples -------- >>> cmp = SokalSneathIV() >>> cmp.sim('cat', 'hat') 0.7487179487179487 >>> cmp.sim('Niall', 'Neil') 0.6810856260030602 >>> cmp.sim('aluminum', 'Catalan') 0.5541986205645999 >>> cmp.sim('ATCG', 'TAGC') 0.496790757381258 .. versionadded:: 0.4.0 """ self._tokenize(src, tar) a = self._intersection_card() b = self._src_only_card() c = self._tar_only_card() d = self._total_complement_card() a_part = 0 if a == 0 else (a / (a + b) + a / (a + c)) d_part = 0 if d == 0 else (d / (b + d) + d / (c + d)) return 0.25 * (a_part + d_part)
if __name__ == '__main__': import doctest doctest.testmod()