Source code for abydos.distance._hawkins_dotson

# Copyright 2018-2020 by Christopher C. Little.
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Hawkins & Dotson similarity

from ._token_distance import _TokenDistance

__all__ = ['HawkinsDotson']

[docs]class HawkinsDotson(_TokenDistance): r"""Hawkins & Dotson similarity. For two sets X and Y and a population N, Hawkins & Dotson similarity :cite:`Hawkins:1973` is the mean of the occurrence agreement and non-occurrence agreement .. math:: sim_{HawkinsDotson}(X, Y) = \frac{1}{2}\cdot\Big( \frac{|X \cap Y|}{|X \cup Y|}+ \frac{|(N \setminus X) \setminus Y|}{|N \setminus (X \cap Y)|} \Big) In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{HawkinsDotson} = \frac{1}{2}\cdot\Big(\frac{a}{a+b+c}+\frac{d}{b+c+d}\Big) .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize HawkinsDotson 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(HawkinsDotson, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return the Hawkins & Dotson 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 Hawkins & Dotson similarity Examples -------- >>> cmp = HawkinsDotson() >>> cmp.sim('cat', 'hat') 0.6641091219096334 >>> cmp.sim('Niall', 'Neil') 0.606635407786303 >>> cmp.sim('aluminum', 'Catalan') 0.5216836734693877 >>> cmp.sim('ATCG', 'TAGC') 0.49362244897959184 .. 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() score = 0.0 if a: score += a / (a + b + c) if d: score += d / (b + c + d) return score / 2
if __name__ == '__main__': import doctest doctest.testmod()