Source code for abydos.distance._hamann

# Copyright 2018-2020 by Christopher C. Little.
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Hamann correlation

from ._token_distance import _TokenDistance

__all__ = ['Hamann']

[docs]class Hamann(_TokenDistance): r"""Hamann correlation. For two sets X and Y and a population N, the Hamann correlation :cite:`Hamann:1961` is .. math:: corr_{Hamann}(X, Y) = \frac{|X \cap Y| + |(N \setminus X) \setminus Y| - |X \setminus Y| - |Y \setminus X|}{|N|} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: corr_{Hamann} = \frac{a+d-b-c}{n} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize Hamann 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(Hamann, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def corr(self, src, tar): """Return the Hamann 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 Hamann correlation Examples -------- >>> cmp = Hamann() >>> cmp.corr('cat', 'hat') 0.9897959183673469 >>> cmp.corr('Niall', 'Neil') 0.9821428571428571 >>> cmp.corr('aluminum', 'Catalan') 0.9617834394904459 >>> cmp.corr('ATCG', 'TAGC') 0.9744897959183674 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 self._tokenize(src, tar) return ( self._intersection_card() + self._total_complement_card() - self._src_only_card() - self._tar_only_card() ) / self._population_unique_card()
[docs] def sim(self, src, tar): """Return the normalized Hamann similarity of two strings. Hamann similarity, which has a range [-1, 1] is normalized to [0, 1] by adding 1 and dividing by 2. Parameters ---------- src : str Source string (or QGrams/Counter objects) for comparison tar : str Target string (or QGrams/Counter objects) for comparison Returns ------- float Normalized Hamann similarity Examples -------- >>> cmp = Hamann() >>> cmp.sim('cat', 'hat') 0.9948979591836735 >>> cmp.sim('Niall', 'Neil') 0.9910714285714286 >>> cmp.sim('aluminum', 'Catalan') 0.9808917197452229 >>> cmp.sim('ATCG', 'TAGC') 0.9872448979591837 .. versionadded:: 0.4.0 """ return (self.corr(src, tar) + 1) / 2
if __name__ == '__main__': import doctest doctest.testmod()