# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Abydos is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Abydos. If not, see <http://www.gnu.org/licenses/>.
"""abydos.distance._dispersion.
Dispersion correlation
"""
from ._token_distance import _TokenDistance
__all__ = ['Dispersion']
[docs]class Dispersion(_TokenDistance):
r"""Dispersion correlation.
For two sets X and Y and a population N, the dispersion
correlation :cite:`IBM:2017` is
.. math::
corr_{dispersion}(X, Y) =
\frac{|X \cap Y| \cdot |(N \setminus X) \setminus Y| -
|X \setminus Y| \cdot |Y \setminus X|}
{|N|^2}
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
corr_{dispersion} =
\frac{ad-bc}{n^2}
.. versionadded:: 0.4.0
"""
def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize Dispersion 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(Dispersion, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
[docs] def corr(self, src, tar):
"""Return the Dispersion 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
Dispersion correlation
Examples
--------
>>> cmp = Dispersion()
>>> cmp.corr('cat', 'hat')
0.002524989587671803
>>> cmp.corr('Niall', 'Neil')
0.002502212619741774
>>> cmp.corr('aluminum', 'Catalan')
0.0011570449105440383
>>> cmp.corr('ATCG', 'TAGC')
-4.06731570179092e-05
.. 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()
n = self._population_unique_card()
admbc = a * d - b * c
if admbc == 0.0:
return 0.0
return admbc / n ** 2
[docs] def sim(self, src, tar):
"""Return the Dispersion 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
Dispersion similarity
Examples
--------
>>> cmp = Dispersion()
>>> cmp.sim('cat', 'hat')
0.5012624947938359
>>> cmp.sim('Niall', 'Neil')
0.5012511063098709
>>> cmp.sim('aluminum', 'Catalan')
0.500578522455272
>>> cmp.sim('ATCG', 'TAGC')
0.499979663421491
.. versionadded:: 0.4.0
"""
return (1 + self.corr(src, tar)) / 2
if __name__ == '__main__':
import doctest
doctest.testmod()