# Source code for abydos.distance._eyraud

# 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._eyraud.

Eyraud similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['Eyraud']

[docs]class Eyraud(_TokenDistance):
r"""Eyraud similarity.

For two sets X and Y and a population N, the Eyraud
similarity :cite:Eyraud:1938 is

.. math::

sim_{Eyraud}(X, Y) =
\frac{|X \cap Y| - |X| \cdot |Y|}
{|X| \cdot |Y| \cdot |N \setminus Y| \cdot |N \setminus X|}

For lack of access to the original, this formula is based on the concurring
formulae presented in :cite:Shi:1993 and :cite:Hubalek:1982.

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

.. math::

sim_{Eyraud} =
\frac{a-(a+b)(a+c)}{(a+b)(a+c)(b+d)(c+d)}

"""

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

[docs]    def sim_score(self, src, tar):
"""Return the Eyraud 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
Eyraud similarity

Examples
--------
>>> cmp = Eyraud()
>>> cmp.sim_score('cat', 'hat')
-1.438198553583169e-06
>>> cmp.sim_score('Niall', 'Neil')
-1.5399964580081465e-06
>>> cmp.sim_score('aluminum', 'Catalan')
-1.6354719962967386e-06
>>> cmp.sim_score('ATCG', 'TAGC')
-1.6478781097519779e-06

"""
self._tokenize(src, tar)

a = self._intersection_card()
b = self._src_only_card()
c = self._tar_only_card()
d = self._total_complement_card()

denom = max(1, a + b) * max(1, c + d) * max(1, a + c) * max(1, b + d)
num = a - (a + b) * (a + c)

return num / denom

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

Examples
--------
>>> cmp = Eyraud()
>>> cmp.sim('cat', 'hat')
1.438198553583169e-06
>>> cmp.sim('Niall', 'Neil')
1.5399964580081465e-06
>>> cmp.sim('aluminum', 'Catalan')
1.6354719962967386e-06
>>> cmp.sim('ATCG', 'TAGC')
1.6478781097519779e-06