# 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 typing import Any, Counter as TCounter, Optional, Sequence, Set, Union
from ._minkowski import Minkowski
from ..tokenizer import _Tokenizer
__all__ = ['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: Optional[
Union[TCounter[str], Sequence[str], Set[str], int]
] = 0,
tokenizer: Optional[_Tokenizer] = None,
intersection_type: str = 'crisp',
**kwargs: Any
) -> None:
"""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: str, tar: str, normalized: bool = False) -> float:
"""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: str, tar: str) -> float:
"""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)
if __name__ == '__main__':
import doctest
doctest.testmod()