# 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._unknown_p.
Unknown H similarity
"""
from ._token_distance import _TokenDistance
__all__ = ['UnknownH']
[docs]class UnknownH(_TokenDistance):
r"""Unknown H similarity.
For two sets X and Y and a population N, Unknown H similarity is a variant
of Fager-McGowan index of affinity :cite:`Fager:1957,Fager:1963`. It uses
minimum rather than maximum in the denominator of the second term, and is
sometimes misidentified as the Fager-McGown index of affinity
(cf. :cite:`Whittaker:1982`, for example).
.. math::
sim_{UnknownH}(X, Y) =
\frac{|X \cap Y|}{\sqrt{|X|\cdot|Y|}} -
\frac{1}{2\sqrt{min(|X|, |Y|)}}
In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n,
this is
.. math::
sim_{UnknownH} =
\frac{a}{\sqrt{(a+b)(a+c)}} - \frac{1}{2\sqrt{min(a+b, a+c)}}
.. versionadded:: 0.4.0
"""
def __init__(
self,
alphabet=None,
tokenizer=None,
intersection_type='crisp',
**kwargs
):
"""Initialize UnknownH 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.
.. versionadded:: 0.4.0
"""
super(UnknownH, self).__init__(
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
[docs] def sim_score(self, src, tar):
"""Return the Unknown H 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
Unknown H similarity
Examples
--------
>>> cmp = UnknownH()
>>> cmp.sim('cat', 'hat')
0.25
>>> cmp.sim('Niall', 'Neil')
0.14154157392013175
>>> cmp.sim('aluminum', 'Catalan')
0.0
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
if not self._src_card() or not self._tar_card():
return 0.0
a = self._intersection_card()
apb = self._src_card()
apc = self._tar_card()
first = a / (apb * apc) ** 0.5 if a else 0.0
second = 1 / (2 * (min(apb, apc) ** 0.5))
return first - second
[docs] def sim(self, src, tar):
r"""Return the normalized Unknown H similarity of two strings.
As this similarity ranges from :math:`(-\inf, 1.0)`, this normalization
simply clamps the value to the range (0.0, 1.0).
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
Returns
-------
float
Normalized Unknown H similarity
Examples
--------
>>> cmp = UnknownH()
>>> cmp.sim('cat', 'hat')
0.25
>>> cmp.sim('Niall', 'Neil')
0.14154157392013175
>>> cmp.sim('aluminum', 'Catalan')
0.0
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
return max(0.0, self.sim_score(src, tar))
if __name__ == '__main__':
import doctest
doctest.testmod()