# Source code for abydos.distance._digby

# 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._digby.

Digby correlation
"""

from ._token_distance import _TokenDistance

__all__ = ['Digby']

[docs]class Digby(_TokenDistance):
r"""Digby correlation.

For two sets X and Y and a population N, Digby's approximation of the
tetrachoric correlation coefficient
:cite:Digby:1983 is

.. math::

corr_{Digby}(X, Y) =
\frac{(|X \cap Y| \cdot |(N \setminus X) \setminus Y|)^\frac{3}{4}-
(|X \setminus Y| \cdot |Y \setminus X|)^\frac{3}{4}}
{(|X \cap Y| \cdot |(N \setminus X) \setminus Y|)^\frac{3}{4} +
(|X \setminus Y| \cdot |Y \setminus X|)^\frac{3}{4}}

In :ref:2x2 confusion table terms <confusion_table>, where a+b+c+d=n,
this is

.. math::

corr_{Digby} =
\frac{ad^\frac{3}{4}-bc^\frac{3}{4}}{ad^\frac{3}{4}+bc^\frac{3}{4}}

.. versionadded:: 0.4.0
"""

def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize Digby 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(Digby, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)

[docs]    def corr(self, src, tar):
"""Return the Digby 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
Digby correlation

Examples
--------
>>> cmp = Digby()
>>> cmp.corr('cat', 'hat')
0.9774244829419212
>>> cmp.corr('Niall', 'Neil')
0.9491281473458171
>>> cmp.corr('aluminum', 'Catalan')
0.7541039303781305
>>> cmp.corr('ATCG', 'TAGC')
-1.0

.. versionadded:: 0.4.0

"""
if src == tar:
return 1.0
if not src or not 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()

num = (a * d) ** 0.75 - (b * c) ** 0.75
if num:
return num / ((a * d) ** 0.75 + (b * c) ** 0.75)
return 0.0

[docs]    def sim(self, src, tar):
"""Return the Digby 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
Digby similarity

Examples
--------
>>> cmp = Digby()
>>> cmp.sim('cat', 'hat')
0.9887122414709606
>>> cmp.sim('Niall', 'Neil')
0.9745640736729085
>>> cmp.sim('aluminum', 'Catalan')
0.8770519651890653
>>> cmp.sim('ATCG', 'TAGC')
0.0

.. versionadded:: 0.4.0

"""
return (1 + self.corr(src, tar)) / 2

if __name__ == '__main__':
import doctest

doctest.testmod()