# Source code for abydos.distance._guttman_lambda_b

# Copyright 2019-2020 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._guttman_lambda_b.

Guttman's Lambda B similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['GuttmanLambdaB']

[docs]class GuttmanLambdaB(_TokenDistance):
r"""Guttman's Lambda B similarity.

For two sets X and Y and a population N, Guttman's :math:\lambda_b
similarity :cite:Guttman:1941 is

.. math::

sim_{Guttman_{\lambda_b}}(X, Y) =
\frac{max(|X \cap Y|, |X \setminus Y|) + max(|Y \setminus X|,
|(N \setminus X) \setminus Y|) - max(|Y|, |N \setminus Y|)}
{|N| - max(|Y|, |N \setminus Y|)}

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

.. math::

sim_{Guttman_{\lambda_b}} =
\frac{max(a, b) + max(c, d) - max(a+c, b+d)}{n - max(a+c, b+d)}

"""

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

[docs]    def sim(self, src, tar):
"""Return the Guttman Lambda B 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
Guttman's Lambda B similarity

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

"""
if src == tar:
return 1.0
if not src or not tar:
return 0.0

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()

num = round(float(max(a, b) + max(c, d) - max(a + c, b + d)), 15)
if num > 1e-8:
return num / float(n - max(a + c, b + d))
return 0.0

if __name__ == '__main__':
import doctest

doctest.testmod()