abydos/distance/_complete_linkage.py
# 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._complete_linkage.
Complete linkage distance
"""
from typing import Any, Optional, cast
from ._distance import _Distance
from ._levenshtein import Levenshtein
from ._token_distance import _TokenDistance
from ..tokenizer import _Tokenizer
__all__ = ['CompleteLinkage']
class CompleteLinkage(_TokenDistance):
r"""Complete linkage distance.
For two multisets X and Y, complete linkage distance
:cite:`Deza:2016` is
.. math::
sim_{CompleteLinkage}(X, Y) =
max_{i \in X, j \in Y} dist(X_i, Y_j)
.. versionadded:: 0.4.0
"""
def __init__(
self,
tokenizer: Optional[_Tokenizer] = None,
metric: Optional[_Distance] = None,
**kwargs: Any
) -> None:
"""Initialize CompleteLinkage instance.
Parameters
----------
tokenizer : _Tokenizer
A tokenizer instance from the :py:mod:`abydos.tokenizer` package
metric : _Distance
A string distance measure class for use in the ``soft`` and
``fuzzy`` variants. (Defaults to Levenshtein distance)
**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(CompleteLinkage, self).__init__(tokenizer=tokenizer, **kwargs)
self._metric = cast(_Distance, metric)
if metric is None:
self._metric = Levenshtein()
def dist_abs(self, src: str, tar: str) -> float:
"""Return the complete linkage distance 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
complete linkage distance
Examples
--------
>>> cmp = CompleteLinkage()
>>> cmp.dist_abs('cat', 'hat')
2
>>> cmp.dist_abs('Niall', 'Neil')
2
>>> cmp.dist_abs('aluminum', 'Catalan')
2
>>> cmp.dist_abs('ATCG', 'TAGC')
2
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
src_tok, tar_tok = self._get_tokens()
max_val = float('-inf')
for term_src in src_tok.keys():
for term_tar in tar_tok.keys():
max_val = max(
max_val, self._metric.dist_abs(term_src, term_tar)
)
return max_val
def dist(self, src: str, tar: str) -> float:
"""Return the normalized complete linkage distance 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 complete linkage distance
Examples
--------
>>> cmp = CompleteLinkage()
>>> cmp.dist('cat', 'hat')
1.0
>>> cmp.dist('Niall', 'Neil')
1.0
>>> cmp.dist('aluminum', 'Catalan')
1.0
>>> cmp.dist('ATCG', 'TAGC')
1.0
.. versionadded:: 0.4.0
"""
self._tokenize(src, tar)
src_tok, tar_tok = self._get_tokens()
max_val = 0.0
for term_src in src_tok.keys():
for term_tar in tar_tok.keys():
max_val = max(max_val, self._metric.dist(term_src, term_tar))
return max_val
if __name__ == '__main__':
import doctest
doctest.testmod()