Source code for abydos.distance._azzoo

# Copyright 2019-2020 by Christopher C. Little.
# This file is part of Abydos.
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"""abydos.distance._azzoo.

AZZOO similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['AZZOO']


[docs]class AZZOO(_TokenDistance): r"""AZZOO similarity. For two sets X and Y, and alphabet N, and a parameter :math:`\sigma`, AZZOO similarity :cite:`Cha:2006` is .. math:: sim_{AZZOO_{\sigma}}(X, Y) = \sum{s_i} where :math:`s_i = 1` if :math:`X_i = Y_i = 1`, :math:`s_i = \sigma` if :math:`X_i = Y_i = 0`, and :math:`s_i = 0` otherwise. In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{AZZOO} = a + \sigma \cdot d .. versionadded:: 0.4.0 """ def __init__( self, sigma=0.5, alphabet=None, tokenizer=None, intersection_type='crisp', **kwargs ): """Initialize AZZOO instance. Parameters ---------- sigma : float Sigma designates the contribution to similarity given by the 0-0 samples in the set. 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(AZZOO, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, **kwargs ) self.set_params(sigma=sigma)
[docs] def sim_score(self, src, tar): """Return the AZZOO 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 AZZOO similarity Examples -------- >>> cmp = AZZOO() >>> cmp.sim_score('cat', 'hat') 391.0 >>> cmp.sim_score('Niall', 'Neil') 389.5 >>> cmp.sim_score('aluminum', 'Catalan') 385.5 >>> cmp.sim_score('ATCG', 'TAGC') 387.0 .. versionadded:: 0.4.0 """ self._tokenize(src, tar) a = self._intersection_card() d = self._total_complement_card() return a + self.params['sigma'] * d
[docs] def sim(self, src, tar): """Return the AZZOO 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 AZZOO similarity Examples -------- >>> cmp = AZZOO() >>> cmp.sim('cat', 'hat') 0.9923857868020305 >>> cmp.sim('Niall', 'Neil') 0.9860759493670886 >>> cmp.sim('aluminum', 'Catalan') 0.9710327455919395 >>> cmp.sim('ATCG', 'TAGC') 0.9809885931558935 .. versionadded:: 0.4.0 """ den = max(self.sim_score(src, src), self.sim_score(tar, tar)) if den == 0.0: return 1.0 return self.sim_score(src, tar) / den
if __name__ == '__main__': import doctest doctest.testmod()