# Source code for abydos.distance._dennis

# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
# Abydos is free software: you can redistribute it and/or modify
# 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 ._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}}}

"""

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.

"""
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

"""
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

"""
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