Source code for abydos.distance._unknown_k

# Copyright 2019-2020 by Christopher C. Little.
# This file is part of Abydos.
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# Abydos is free software: you can redistribute it and/or modify
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"""abydos.distance._unknown_k.

Unknown K distance
"""

from ._token_distance import _TokenDistance

__all__ = ['UnknownK']


[docs]class UnknownK(_TokenDistance): r"""Unknown K distance. For two sets X and Y and a population N, Unknown K distance, which :cite:`SequentiX:2018` attributes to "Excoffier" but could not be located, is .. math:: dist_{UnknownK}(X, Y) = |N| \cdot (1 - \frac{|X \cap Y|}{|N|}) In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: dist_{UnknownK} = n \cdot (1 - \frac{a}{n}) .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize UnknownK 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(UnknownK, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def dist_abs(self, src, tar): """Return the Unknown K distance 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 Unknown K distance Examples -------- >>> cmp = UnknownK() >>> cmp.dist_abs('cat', 'hat') 782.0 >>> cmp.dist_abs('Niall', 'Neil') 782.0 >>> cmp.dist_abs('aluminum', 'Catalan') 784.0 >>> cmp.dist_abs('ATCG', 'TAGC') 784.0 .. versionadded:: 0.4.0 """ self._tokenize(src, tar) a = self._intersection_card() n = self._population_unique_card() if not n: return 0.0 return n * (1 - a / n)
[docs] def dist(self, src, tar): """Return the normalized Unknown K distance 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 Normalized Unknown K distance Examples -------- >>> cmp = UnknownK() >>> cmp.dist('cat', 'hat') 0.9974489795918368 >>> cmp.dist('Niall', 'Neil') 0.9974489795918368 >>> cmp.dist('aluminum', 'Catalan') 0.9987261146496815 >>> cmp.dist('ATCG', 'TAGC') 1.0 .. versionadded:: 0.4.0 """ score = self.dist_abs(src, tar) norm = self._population_unique_card() if score: return score / norm return 0.0
if __name__ == '__main__': import doctest doctest.testmod()