# Source code for abydos.distance._harris_lahey

# 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._harris_lahey.

Harris & Lahey similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['HarrisLahey']

[docs]class HarrisLahey(_TokenDistance):
r"""Harris & Lahey similarity.

For two sets X and Y and a population N, Harris & Lahey similarity
:cite:Harris:1978 is

.. math::

sim_{HarrisLahey}(X, Y) =
\frac{|X \cap Y|}{|X \cup Y|}\cdot
\frac{|N \setminus Y| + |N \setminus X|}{2|N|}+
\frac{|(N \setminus X) \setminus Y|}{|N \setminus (X \cap Y)|}\cdot
\frac{|X| + |Y|}{2|N|}

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

.. math::

sim_{HarrisLahey} =
\frac{a}{a+b+c}\cdot\frac{2d+b+c}{2n}+
\frac{d}{d+b+c}\cdot\frac{2a+b+c}{2n}

Notes
-----
Most catalogs of similarity coefficients
:cite:Warrens:2008,Morris:2012,Xiang:2013 omit the :math:n terms in the
denominators, but the worked example in :cite:Harris:1978 makes it clear
that this is intended in the original.

"""

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

"""
super(HarrisLahey, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)

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

Examples
--------
>>> cmp = HarrisLahey()
>>> cmp.sim('cat', 'hat')
0.3367085964820711
>>> cmp.sim('Niall', 'Neil')
0.22761577457069784
>>> cmp.sim('aluminum', 'Catalan')
0.07244410503054725
>>> cmp.sim('ATCG', 'TAGC')
0.006296204706372345

"""
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()
n = self._population_unique_card()

score = 0.0
if a and (d + b + c):
score += a / (a + b + c) * (2 * d + b + c) / (2 * n)
if d and (a + b + c):
score += d / (d + b + c) * (2 * a + b + c) / (2 * n)
return score

if __name__ == '__main__':
import doctest

doctest.testmod()