# 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._brainerd_robinson.
Brainerd-Robinson similarity
"""
from ._token_distance import _TokenDistance
__all__ = ['BrainerdRobinson']
[docs]class BrainerdRobinson(_TokenDistance):
r"""Brainerd-Robinson similarity.
For two multisets X and Y drawn from an alphabet S, Brainerd-Robinson
similarity :cite:`Robinson:1951,Brainerd:1951` is
.. math::
sim_{BrainerdRobinson}(X, Y) =
200 - 100 \cdot \sum_{i \in S} |\frac{X_i}{\sum_{i \in S} |X_i|} -
\frac{Y_i}{\sum_{i \in S} |Y_i|}|
.. versionadded:: 0.4.0
"""
def __init__(self, tokenizer=None, **kwargs):
"""Initialize BrainerdRobinson instance.
Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:`abydos.tokenizer` package
**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.
.. versionadded:: 0.4.0
"""
super(BrainerdRobinson, self).__init__(tokenizer=tokenizer, **kwargs)
[docs] def sim_score(self, src, tar):
"""Return the Brainerd-Robinson 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
Brainerd-Robinson similarity
Examples
--------
>>> cmp = BrainerdRobinson()
>>> cmp.sim_score('cat', 'hat')
100.0
>>> cmp.sim_score('Niall', 'Neil')
66.66666666666669
>>> cmp.sim_score('aluminum', 'Catalan')
22.2222222222222
>>> cmp.sim_score('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
alphabet = self._total().keys()
src_card = max(1, self._src_card())
tar_card = max(1, self._tar_card())
score = 200.0 - 100.0 * sum(
abs(
self._src_tokens[tok] / src_card
- self._tar_tokens[tok] / tar_card
)
for tok in alphabet
)
if score < 1e-13:
score = 0.0
return score
[docs] def sim(self, src, tar):
"""Return the normalized Brainerd-Robinson 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 Brainerd-Robinson similarity
Examples
--------
>>> cmp = BrainerdRobinson()
>>> cmp.sim('cat', 'hat')
0.5
>>> cmp.sim('Niall', 'Neil')
0.3333333333333334
>>> cmp.sim('aluminum', 'Catalan')
0.111111111111111
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
return self.sim_score(src, tar) / 200.0
if __name__ == '__main__':
import doctest
doctest.testmod()