Source code for abydos.distance._koppen_i

# Copyright 2018-2020 by Christopher C. Little.
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"""abydos.distance._koppen_i.

Köppen I correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['KoppenI']


[docs]class KoppenI(_TokenDistance): r"""Köppen I correlation. For two sets X and Y and an alphabet N, provided that :math:`|X| = |Y|`, Köppen I correlation :cite:`Koppen:1870,Goodman:1959` is .. math:: corr_{KoppenI}(X, Y) = \frac{|X| \cdot |N \setminus X| - |X \setminus Y|} {|X| \cdot |N \setminus X|} To support cases where :math:`|X| \neq |Y|`, this class implements a slight variation, while still providing the expected results when :math:`|X| = |Y|`: .. math:: corr_{KoppenI}(X, Y) = \frac{\frac{|X|+|Y|}{2} \cdot \frac{|N \setminus X|+|N \setminus Y|}{2}- \frac{|X \triangle Y|}{2}} {\frac{|X|+|Y|}{2} \cdot \frac{|N \setminus X|+|N \setminus Y|}{2}} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{KoppenI} = \frac{\frac{2a+b+c}{2} \cdot \frac{2d+b+c}{2}- \frac{b+c}{2}} {\frac{2a+b+c}{2} \cdot \frac{2d+b+c}{2}} Notes ----- In the usual case all of the above values should be proportional to the total number of samples n. I.e., a, b, c, d, & n should all be divided by n prior to calculating the coefficient. This class's default normalizer is, accordingly, 'proportional'. .. versionadded:: 0.4.0 """ def __init__( self, alphabet=None, tokenizer=None, intersection_type='crisp', normalizer='proportional', **kwargs ): """Initialize KoppenI 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. normalizer : str Specifies the normalization type. See :ref:`normalizer <alphabet>` 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(KoppenI, self).__init__( alphabet=alphabet, tokenizer=tokenizer, intersection_type=intersection_type, normalizer=normalizer, **kwargs )
[docs] def corr(self, src, tar): """Return the Köppen I 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 Köppen I correlation Examples -------- >>> cmp = KoppenI() >>> cmp.corr('cat', 'hat') 0.49615384615384617 >>> cmp.corr('Niall', 'Neil') 0.3575056927658083 >>> cmp.corr('aluminum', 'Catalan') 0.1068520131813188 >>> cmp.corr('ATCG', 'TAGC') -0.006418485237483896 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 self._tokenize(src, tar) a = self._intersection_card() b = self._src_only_card() c = self._tar_only_card() d = self._total_complement_card() abac_dbdc_mean_prod = (2 * a + b + c) * (2 * d + b + c) / 4 num = abac_dbdc_mean_prod - (b + c) / 2 if num: return num / abac_dbdc_mean_prod return 0.0
[docs] def sim(self, src, tar): """Return the Köppen I 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 Köppen I similarity Examples -------- >>> cmp = KoppenI() >>> cmp.sim('cat', 'hat') 0.7480769230769231 >>> cmp.sim('Niall', 'Neil') 0.6787528463829041 >>> cmp.sim('aluminum', 'Catalan') 0.5534260065906594 >>> cmp.sim('ATCG', 'TAGC') 0.49679075738125805 .. versionadded:: 0.4.0 """ return (1.0 + self.corr(src, tar)) / 2.0
if __name__ == '__main__': import doctest doctest.testmod()