# 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._manhattan.
Manhattan distance & similarity
"""
from deprecation import deprecated
from ._minkowski import Minkowski
from .. import __version__
__all__ = ['Manhattan', 'dist_manhattan', 'manhattan', 'sim_manhattan']
[docs]class Manhattan(Minkowski):
"""Manhattan distance.
Manhattan distance is the city-block or taxi-cab distance, equivalent
to Minkowski distance in :math:`L^1`-space.
.. versionadded:: 0.3.6
"""
def __init__(
self, alphabet=0, tokenizer=None, intersection_type='crisp', **kwargs
):
"""Initialize Manhattan instance.
Parameters
----------
alphabet : collection or int
The values or size of the alphabet
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(Manhattan, self).__init__(
pval=1,
alphabet=alphabet,
tokenizer=tokenizer,
intersection_type=intersection_type,
**kwargs
)
[docs] def dist_abs(self, src, tar, normalized=False):
"""Return the Manhattan distance between two strings.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
normalized : bool
Normalizes to [0, 1] if True
Returns
-------
float
The Manhattan distance
Examples
--------
>>> cmp = Manhattan()
>>> cmp.dist_abs('cat', 'hat')
4.0
>>> cmp.dist_abs('Niall', 'Neil')
7.0
>>> cmp.dist_abs('Colin', 'Cuilen')
9.0
>>> cmp.dist_abs('ATCG', 'TAGC')
10.0
.. versionadded:: 0.3.0
.. versionchanged:: 0.3.6
Encapsulated in class
"""
return super(Manhattan, self).dist_abs(src, tar, normalized=normalized)
[docs] def dist(self, src, tar):
"""Return the normalized Manhattan distance between two strings.
The normalized Manhattan distance is a distance metric in
:math:`L^1`-space, normalized to [0, 1].
This is identical to Canberra distance.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
Returns
-------
float
The normalized Manhattan distance
Examples
--------
>>> cmp = Manhattan()
>>> cmp.dist('cat', 'hat')
0.5
>>> round(cmp.dist('Niall', 'Neil'), 12)
0.636363636364
>>> round(cmp.dist('Colin', 'Cuilen'), 12)
0.692307692308
>>> cmp.dist('ATCG', 'TAGC')
1.0
.. versionadded:: 0.3.0
.. versionchanged:: 0.3.6
Encapsulated in class
"""
return self.dist_abs(src, tar, normalized=True)
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the Manhattan.dist_abs method instead.',
)
def manhattan(src, tar, qval=2, normalized=False, alphabet=None):
"""Return the Manhattan distance between two strings.
This is a wrapper for :py:meth:`Manhattan.dist_abs`.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
qval : int
The length of each q-gram
normalized : bool
Normalizes to [0, 1] if True
alphabet : collection or int
The values or size of the alphabet
Returns
-------
float
The Manhattan distance
Examples
--------
>>> manhattan('cat', 'hat')
4.0
>>> manhattan('Niall', 'Neil')
7.0
>>> manhattan('Colin', 'Cuilen')
9.0
>>> manhattan('ATCG', 'TAGC')
10.0
.. versionadded:: 0.3.0
"""
return Manhattan(alphabet=alphabet, qval=qval).dist_abs(
src, tar, normalized=normalized
)
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the Manhattan.dist method instead.',
)
def dist_manhattan(src, tar, qval=2, alphabet=0):
"""Return the normalized Manhattan distance between two strings.
This is a wrapper for :py:meth:`Manhattan.dist`.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
qval : int
The length of each q-gram
alphabet : collection or int
The values or size of the alphabet
Returns
-------
float
The normalized Manhattan distance
Examples
--------
>>> dist_manhattan('cat', 'hat')
0.5
>>> round(dist_manhattan('Niall', 'Neil'), 12)
0.636363636364
>>> round(dist_manhattan('Colin', 'Cuilen'), 12)
0.692307692308
>>> dist_manhattan('ATCG', 'TAGC')
1.0
.. versionadded:: 0.3.0
"""
return Manhattan(alphabet=alphabet, qval=qval).dist(src, tar)
[docs]@deprecated(
deprecated_in='0.4.0',
removed_in='0.6.0',
current_version=__version__,
details='Use the Manhattan.sim method instead.',
)
def sim_manhattan(src, tar, qval=2, alphabet=0):
"""Return the normalized Manhattan similarity of two strings.
This is a wrapper for :py:meth:`Manhattan.sim`.
Parameters
----------
src : str
Source string (or QGrams/Counter objects) for comparison
tar : str
Target string (or QGrams/Counter objects) for comparison
qval : int
The length of each q-gram
alphabet : collection or int
The values or size of the alphabet
Returns
-------
float
The normalized Manhattan similarity
Examples
--------
>>> sim_manhattan('cat', 'hat')
0.5
>>> round(sim_manhattan('Niall', 'Neil'), 12)
0.363636363636
>>> round(sim_manhattan('Colin', 'Cuilen'), 12)
0.307692307692
>>> sim_manhattan('ATCG', 'TAGC')
0.0
.. versionadded:: 0.3.0
"""
return Manhattan(alphabet=alphabet, qval=qval).sim(src, tar)
if __name__ == '__main__':
import doctest
doctest.testmod()