Source code for abydos.distance._guttman_lambda_a

# Copyright 2019-2020 by Christopher C. Little.
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Guttman's Lambda A similarity

from ._token_distance import _TokenDistance

__all__ = ['GuttmanLambdaA']

[docs]class GuttmanLambdaA(_TokenDistance): r"""Guttman's Lambda A similarity. For two sets X and Y and a population N, Guttman's :math:`\lambda_a` similarity :cite:`Guttman:1941` is .. math:: sim_{Guttman_{\lambda_a}}(X, Y) = \frac{max(|X \cap Y|, |Y \setminus X|) + max(|X \setminus Y|, |(N \setminus X) \setminus Y|) - max(|X|, |N \setminus X|)} {|N| - max(|X|, |N \setminus X|)} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{Guttman_{\lambda_a}} = \frac{max(a, c) + max(b, d) - max(a+b, c+d)}{n - max(a+b, c+d)} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize GuttmanLambdaA 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(GuttmanLambdaA, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim(self, src, tar): """Return the Guttman Lambda A 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 Guttman's Lambda A similarity Examples -------- >>> cmp = GuttmanLambdaA() >>> cmp.sim('cat', 'hat') 0.0 >>> cmp.sim('Niall', 'Neil') 0.0 >>> cmp.sim('aluminum', 'Catalan') 0.0 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 if not src or not tar: return 0.0 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 = round(float(max(a, c) + max(b, d) - max(a + b, c + d)), 15) if num > 1e-8: return num / float(n - max(a + b, c + d)) return 0.0
if __name__ == '__main__': import doctest doctest.testmod()