# Copyright 2014-2020 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._mlipns.
The distance.hamming module implements Hamming and related distance functions.
"""
from deprecation import deprecated
from ._distance import _Distance
from ._hamming import Hamming
from .. import __version__
__all__ = ['MLIPNS', 'dist_mlipns', 'sim_mlipns']
[docs]class MLIPNS(_Distance):
"""MLIPNS similarity.
Modified Language-Independent Product Name Search (MLIPNS) is described in
:cite:`Shannaq:2010`. This function returns only 1.0 (similar) or 0.0
(not similar). LIPNS similarity is identical to normalized Hamming
similarity.
.. versionadded:: 0.3.6
"""
_hamming = Hamming(diff_lens=True)
def __init__(self, threshold=0.25, max_mismatches=2, **kwargs):
"""Initialize MLIPNS instance.
Parameters
----------
threshold : float
A number [0, 1] indicating the maximum similarity score, below
which the strings are considered 'similar' (0.25 by default)
max_mismatches : int
A number indicating the allowable number of mismatches to remove
before declaring two strings not similar (2 by default)
**kwargs
Arbitrary keyword arguments
.. versionadded:: 0.4.0
"""
super(MLIPNS, self).__init__(**kwargs)
self._threshold = threshold
self._max_mismatches = max_mismatches
[docs] def sim(self, src, tar):
"""Return the MLIPNS similarity of two strings.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
Returns
-------
float
MLIPNS similarity
Examples
--------
>>> sim_mlipns('cat', 'hat')
1.0
>>> sim_mlipns('Niall', 'Neil')
0.0
>>> sim_mlipns('aluminum', 'Catalan')
0.0
>>> sim_mlipns('ATCG', 'TAGC')
0.0
.. versionadded:: 0.1.0
.. versionchanged:: 0.3.6
Encapsulated in class
"""
if tar == src:
return 1.0
if not src or not tar:
return 0.0
mismatches = 0
ham = self._hamming.dist_abs(src, tar)
max_length = max(len(src), len(tar))
while src and tar and mismatches <= self._max_mismatches:
if (
max_length < 1
or (1 - (max_length - ham) / max_length) <= self._threshold
):
return 1.0
else:
mismatches += 1
ham -= 1
max_length -= 1
if max_length < 1:
return 1.0
return 0.0
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the MLIPNS.sim method instead.',
)
def sim_mlipns(src, tar, threshold=0.25, max_mismatches=2):
"""Return the MLIPNS similarity of two strings.
This is a wrapper for :py:meth:`MLIPNS.sim`.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
threshold : float
A number [0, 1] indicating the maximum similarity score, below which
the strings are considered 'similar' (0.25 by default)
max_mismatches : int
A number indicating the allowable number of mismatches to remove before
declaring two strings not similar (2 by default)
Returns
-------
float
MLIPNS similarity
Examples
--------
>>> sim_mlipns('cat', 'hat')
1.0
>>> sim_mlipns('Niall', 'Neil')
0.0
>>> sim_mlipns('aluminum', 'Catalan')
0.0
>>> sim_mlipns('ATCG', 'TAGC')
0.0
.. versionadded:: 0.1.0
"""
return MLIPNS(threshold, max_mismatches).sim(src, tar)
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the MLIPNS.dist method instead.',
)
def dist_mlipns(src, tar, threshold=0.25, max_mismatches=2):
"""Return the MLIPNS distance between two strings.
This is a wrapper for :py:meth:`MLIPNS.dist`.
Parameters
----------
src : str
Source string for comparison
tar : str
Target string for comparison
threshold : float
A number [0, 1] indicating the maximum similarity score, below which
the strings are considered 'similar' (0.25 by default)
max_mismatches : int
A number indicating the allowable number of mismatches to remove before
declaring two strings not similar (2 by default)
Returns
-------
float
MLIPNS distance
Examples
--------
>>> dist_mlipns('cat', 'hat')
0.0
>>> dist_mlipns('Niall', 'Neil')
1.0
>>> dist_mlipns('aluminum', 'Catalan')
1.0
>>> dist_mlipns('ATCG', 'TAGC')
1.0
.. versionadded:: 0.1.0
"""
return MLIPNS(threshold, max_mismatches).dist(src, tar)
if __name__ == '__main__':
import doctest
doctest.testmod()