# Source code for abydos.distance._ample

# 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._ample.

AMPLE similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['AMPLE']

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

The AMPLE similarity :cite:Dallmeier:2005,Abreu:2007 is defined in
getAverageSequenceWeight() in the AverageSequenceWeightEvaluator.java file
of AMPLE's source code. For two sets X and Y and a population N, it is

.. math::

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

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

.. math::

sim_{AMPLE} =
\big|\frac{a}{a+b}-\frac{c}{c+d}\big|

Notes
-----
This measure is asymmetric. The first ratio considers how similar the two
strings are, while the second considers how dissimilar the second string
is. As a result, both very similar and very dissimilar strings will score
high on this measure, provided the unique aspects are present chiefly
in the latter string.

"""

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

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

Examples
--------
>>> cmp = AMPLE()
>>> cmp.sim('cat', 'hat')
0.49743589743589745
>>> cmp.sim('Niall', 'Neil')
0.32947729220222793
>>> cmp.sim('aluminum', 'Catalan')
0.10209049255441008
>>> cmp.sim('ATCG', 'TAGC')
0.006418485237483954

"""
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()

# If the denominators are 0, set them to 1.
# This is a deviation from the formula, but prevents division by zero
# while retaining the contribution of the other ratio.
if a + b == 0:
b = 1
if c + d == 0:
d = 1

return abs((a / (a + b)) - (c / (c + d)))

if __name__ == '__main__':
import doctest

doctest.testmod()