Source code for abydos.distance._eyraud

# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
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"""abydos.distance._eyraud.

Eyraud similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['Eyraud']


[docs]class Eyraud(_TokenDistance): r"""Eyraud similarity. For two sets X and Y and a population N, the Eyraud similarity :cite:`Eyraud:1938` is .. math:: sim_{Eyraud}(X, Y) = \frac{|X \cap Y| - |X| \cdot |Y|} {|X| \cdot |Y| \cdot |N \setminus Y| \cdot |N \setminus X|} For lack of access to the original, this formula is based on the concurring formulae presented in :cite:`Shi:1993` and :cite:`Hubalek:1982`. In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{Eyraud} = \frac{a-(a+b)(a+c)}{(a+b)(a+c)(b+d)(c+d)} .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize Eyraud 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(Eyraud, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim_score(self, src, tar): """Return the Eyraud 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 Eyraud similarity Examples -------- >>> cmp = Eyraud() >>> cmp.sim_score('cat', 'hat') -1.438198553583169e-06 >>> cmp.sim_score('Niall', 'Neil') -1.5399964580081465e-06 >>> cmp.sim_score('aluminum', 'Catalan') -1.6354719962967386e-06 >>> cmp.sim_score('ATCG', 'TAGC') -1.6478781097519779e-06 .. 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() denom = max(1, a + b) * max(1, c + d) * max(1, a + c) * max(1, b + d) num = a - (a + b) * (a + c) return num / denom
[docs] def sim(self, src, tar): """Return the normalized Eyraud 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 Normalized Eyraud similarity Examples -------- >>> cmp = Eyraud() >>> cmp.sim('cat', 'hat') 1.438198553583169e-06 >>> cmp.sim('Niall', 'Neil') 1.5399964580081465e-06 >>> cmp.sim('aluminum', 'Catalan') 1.6354719962967386e-06 >>> cmp.sim('ATCG', 'TAGC') 1.6478781097519779e-06 .. versionadded:: 0.4.0 """ return 0.0 - self.sim_score(src, tar)
if __name__ == '__main__': import doctest doctest.testmod()