# Copyright 2019-2020 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._tulloss_t.
Tulloss' T similarity
"""
from ._token_distance import _TokenDistance
from ._tulloss_r import TullossR
from ._tulloss_s import TullossS
from ._tulloss_u import TullossU
__all__ = ['TullossT']
[docs]class TullossT(_TokenDistance):
r"""Tulloss' T similarity.
For two sets X and Y and a population N, Tulloss' T similarity
:cite:`Tulloss:1997` is
.. math::
\begin{array}{l}
sim_{Tulloss_T}(X, Y) = \sqrt{sim_{Tulloss_U}(X, Y) \cdot
sim_{Tulloss_S}(X, Y) \cdot sim_{Tulloss_R}(X, Y)}
= \sqrt{
log_2(1+\frac{min(|X \setminus Y|, |Y \setminus X|)+|X \cap Y|}
{max(|X \setminus Y|, |Y \setminus X|)+|X \cap Y|}) \cdot
\frac{1}{\sqrt{log_2(2+\frac{min(|X \setminus Y|, |Y \setminus X|)}
{|X \cap Y|+1})}} \cdot
\frac{log(1+\frac{|X \cap Y|}{|X|}) \cdot log(1+\frac{|X \cap Y|}
{|Y|})}{log^2(2)}}
\end{array}
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim_{Tulloss_T} = \sqrt{
log_2\Big(1+\frac{min(b, c)+a}{max(b, c)+a}\Big) \cdot
\frac{1}{\sqrt{log_2(2+\frac{min(b,c)}{a+1})}} \cdot
\frac{log(1+\frac{a}{a+b}) \cdot log(1+\frac{a}{a+c})}{log^2(2)}}
.. versionadded:: 0.4.0
"""
def __init__(self, tokenizer=None, intersection_type='crisp', **kwargs):
"""Initialize TullossT instance.
Parameters
----------
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.
.. versionadded:: 0.4.0
"""
super(TullossT, self).__init__(
tokenizer=tokenizer, intersection_type=intersection_type, **kwargs
)
self._r = TullossR()
self._s = TullossS()
self._u = TullossU()
[docs] def sim(self, src, tar):
"""Return Tulloss' T 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
Tulloss' T similarity
Examples
--------
>>> cmp = TullossT()
>>> cmp.sim('cat', 'hat')
0.5363348766461724
>>> cmp.sim('Niall', 'Neil')
0.3740873705689327
>>> cmp.sim('aluminum', 'Catalan')
0.1229300783095269
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
r = self._r.sim(src, tar)
s = self._s.sim(src, tar)
u = self._u.sim(src, tar)
return (r * s * u) ** 0.5
if __name__ == '__main__':
import doctest
doctest.testmod()