# -*- coding: utf-8 -*-
# Copyright 2018-2019 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._dennis.
Dennis similarity
"""
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
)
from ._token_distance import _TokenDistance
__all__ = ['Dennis']
[docs]class Dennis(_TokenDistance):
r"""Dennis similarity.
For two sets X and Y and a population N, Dennis similarity
:cite:`Dennis:1965` is
.. math::
sim_{Dennis}(X, Y) =
\frac{|X \cap Y| - \frac{|X| \cdot |Y|}{|N|}}
{\sqrt{\frac{|X|\cdot|Y|}{|N|}}}
This is the fourth of Dennis' association measures, and that which she
claims is the best of the four.
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim_{Dennis} =
\frac{a-\frac{(a+b)(a+c)}{n}}{\sqrt{\frac{(a+b)(a+c)}{n}}}
.. versionadded:: 0.4.0
"""
def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize Dennis 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(Dennis, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
[docs] def sim_score(self, src, tar):
"""Return the Dennis 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
Dennis similarity
Examples
--------
>>> cmp = Dennis()
>>> cmp.sim_score('cat', 'hat')
13.857142857142858
>>> cmp.sim_score('Niall', 'Neil')
10.028539207654113
>>> cmp.sim_score('aluminum', 'Catalan')
2.9990827802847835
>>> cmp.sim_score('ATCG', 'TAGC')
-0.17857142857142858
.. versionadded:: 0.4.0
"""
if not src and not tar:
return 0.0
self._tokenize(src, tar)
a = self._intersection_card()
abacn = (
self._src_card()
* self._tar_card()
/ self._population_unique_card()
)
num = a - abacn
if num == 0:
return 0.0
return num / abacn ** 0.5
[docs] def corr(self, src, tar):
"""Return the Dennis 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
Dennis correlation
Examples
--------
>>> cmp = Dennis()
>>> cmp.corr('cat', 'hat')
0.494897959183673
>>> cmp.corr('Niall', 'Neil')
0.358162114559075
>>> cmp.corr('aluminum', 'Catalan')
0.107041854561785
>>> cmp.corr('ATCG', 'TAGC')
-0.006377551020408
.. versionadded:: 0.4.0
"""
score = self.sim_score(src, tar)
if score == 0.0:
return 0.0
return round(score / self._population_unique_card() ** 0.5, 15)
[docs] def sim(self, src, tar):
"""Return the normalized Dennis 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 Dennis similarity
Examples
--------
>>> cmp = Dennis()
>>> cmp.sim('cat', 'hat')
0.6632653061224487
>>> cmp.sim('Niall', 'Neil')
0.5721080763727167
>>> cmp.sim('aluminum', 'Catalan')
0.4046945697078567
>>> cmp.sim('ATCG', 'TAGC')
0.32908163265306134
.. versionadded:: 0.4.0
"""
return (0.5 + self.corr(src, tar)) / 1.5
if __name__ == '__main__':
import doctest
doctest.testmod()