Source code for abydos.distance._complete_linkage

# 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
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"""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']


[docs]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()
[docs] 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
[docs] 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()