# 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._quantitative_jaccard.
Quantitative Jaccard similarity
"""
from ._token_distance import _TokenDistance
__all__ = ['QuantitativeJaccard']
[docs]class QuantitativeJaccard(_TokenDistance):
r"""Quantitative Jaccard similarity.
For two multisets X and Y drawn from an alphabet S, Quantitative Jaccard
similarity is
.. math::
sim_{QuantitativeJaccard}(X, Y) =
\frac{\sum_{i \in S} X_iY_i}
{\sum_{i \in S} X_i^2 + \sum_{i \in S} Y_i^2 -
\sum_{i \in S} X_iY_i}
.. versionadded:: 0.4.0
"""
def __init__(self, tokenizer=None, **kwargs):
"""Initialize QuantitativeJaccard 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(QuantitativeJaccard, self).__init__(
tokenizer=tokenizer, **kwargs
)
[docs] def sim(self, src, tar):
"""Return the Quantitative Jaccard 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
Quantitative Jaccard similarity
Examples
--------
>>> cmp = QuantitativeJaccard()
>>> cmp.sim('cat', 'hat')
0.3333333333333333
>>> cmp.sim('Niall', 'Neil')
0.2222222222222222
>>> cmp.sim('aluminum', 'Catalan')
0.05555555555555555
>>> cmp.sim('ATCG', 'TAGC')
0.0
.. versionadded:: 0.4.0
"""
if src == tar:
return 1.0
self._tokenize(src, tar)
alphabet = self._total().keys()
product = sum(
self._src_tokens[tok] * self._tar_tokens[tok] for tok in alphabet
)
return product / (
sum(
self._src_tokens[tok] * self._src_tokens[tok]
for tok in alphabet
)
+ sum(
self._tar_tokens[tok] * self._tar_tokens[tok]
for tok in alphabet
)
- product
)
if __name__ == '__main__':
import doctest
doctest.testmod()