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# Copyright 2014-2020 by Christopher C. Little. |
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# This file is part of Abydos. |
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# |
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# Abydos is free software: you can redistribute it and/or modify |
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# it under the terms of the GNU General Public License as published by |
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# the Free Software Foundation, either version 3 of the License, or |
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# (at your option) any later version. |
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# |
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# Abydos is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU General Public License for more details. |
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# |
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# You should have received a copy of the GNU General Public License |
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# along with Abydos. If not, see <http://www.gnu.org/licenses/>. |
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"""abydos.distance._jaro_winkler. |
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The distance._JaroWinkler module implements distance metrics based on |
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:cite:`Jaro:1989` and subsequent works: |
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- Jaro distance |
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- Jaro-Winkler distance |
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""" |
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from ._distance import _Distance |
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from ..tokenizer import QGrams |
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1 |
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__all__ = ['JaroWinkler'] |
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class JaroWinkler(_Distance): |
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"""Jaro-Winkler distance. |
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Jaro(-Winkler) distance is a string edit distance initially proposed by |
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Jaro and extended by Winkler :cite:`Jaro:1989,Winkler:1990`. |
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This is Python based on the C code for strcmp95: |
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http://web.archive.org/web/20110629121242/http://www.census.gov/geo/msb/stand/strcmp.c |
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:cite:`Winkler:1994`. The above file is a US Government publication and, |
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accordingly, in the public domain. |
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.. versionadded:: 0.3.6 |
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""" |
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def __init__( |
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self, |
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qval=1, |
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mode='winkler', |
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long_strings=False, |
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boost_threshold=0.7, |
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scaling_factor=0.1, |
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**kwargs |
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): |
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"""Initialize JaroWinkler instance. |
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Parameters |
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---------- |
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qval : int |
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The length of each q-gram (defaults to 1: character-wise matching) |
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mode : str |
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Indicates which variant of this distance metric to compute: |
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- ``winkler`` -- computes the Jaro-Winkler distance (default) |
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which increases the score for matches near the start of the |
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word |
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- ``jaro`` -- computes the Jaro distance |
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long_strings : bool |
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Set to True to "Increase the probability of a match when the number |
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of matched characters is large. This option allows for a little |
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more tolerance when the strings are large. It is not an appropriate |
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test when comparing fixed length fields such as phone and social |
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security numbers." (Used in 'winkler' mode only.) |
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boost_threshold : float |
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A value between 0 and 1, below which the Winkler boost is not |
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applied (defaults to 0.7). (Used in 'winkler' mode only.) |
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scaling_factor : float |
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A value between 0 and 0.25, indicating by how much to boost scores |
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for matching prefixes (defaults to 0.1). (Used in 'winkler' mode |
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only.) |
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.. versionadded:: 0.4.0 |
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""" |
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super(JaroWinkler, self).__init__(**kwargs) |
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self._qval = qval |
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self._mode = mode |
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self._long_strings = long_strings |
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self._boost_threshold = boost_threshold |
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self._scaling_factor = scaling_factor |
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def sim(self, src, tar): |
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"""Return the Jaro or Jaro-Winkler similarity of two strings. |
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Parameters |
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---------- |
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src : str |
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Source string for comparison |
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tar : str |
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Target string for comparison |
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Returns |
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------- |
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float |
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Jaro or Jaro-Winkler similarity |
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Raises |
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------ |
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ValueError |
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Unsupported boost_threshold assignment; boost_threshold must be |
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between 0 and 1. |
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ValueError |
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Unsupported scaling_factor assignment; scaling_factor must be |
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between 0 and 0.25.' |
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Examples |
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-------- |
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>>> cmp = JaroWinkler() |
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>>> round(cmp.sim('cat', 'hat'), 12) |
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0.777777777778 |
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>>> round(cmp.sim('Niall', 'Neil'), 12) |
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0.805 |
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>>> round(cmp.sim('aluminum', 'Catalan'), 12) |
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0.60119047619 |
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>>> round(cmp.sim('ATCG', 'TAGC'), 12) |
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0.833333333333 |
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>>> cmp = JaroWinkler(mode='jaro') |
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>>> round(cmp.sim('cat', 'hat'), 12) |
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0.777777777778 |
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>>> round(cmp.sim('Niall', 'Neil'), 12) |
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0.783333333333 |
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>>> round(cmp.sim('aluminum', 'Catalan'), 12) |
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0.60119047619 |
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>>> round(cmp.sim('ATCG', 'TAGC'), 12) |
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0.833333333333 |
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.. versionadded:: 0.1.0 |
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.. versionchanged:: 0.3.6 |
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Encapsulated in class |
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""" |
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if self._mode == 'winkler': |
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if self._boost_threshold > 1 or self._boost_threshold < 0: |
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raise ValueError( |
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'Unsupported boost_threshold assignment; ' |
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+ 'boost_threshold must be between 0 and 1.' |
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) |
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if self._scaling_factor > 0.25 or self._scaling_factor < 0: |
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raise ValueError( |
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'Unsupported scaling_factor assignment; ' |
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+ 'scaling_factor must be between 0 and 0.25.' |
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) |
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if src == tar: |
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return 1.0 |
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src = QGrams(self._qval).tokenize(src.strip()).get_list() |
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tar = QGrams(self._qval).tokenize(tar.strip()).get_list() |
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lens = len(src) |
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lent = len(tar) |
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# If either string is blank - return - added in Version 2 |
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if lens == 0 or lent == 0: |
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return 0.0 |
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if lens > lent: |
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search_range = lens |
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minv = lent |
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else: |
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search_range = lent |
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minv = lens |
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# Zero out the flags |
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src_flag = [0] * search_range |
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tar_flag = [0] * search_range |
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search_range = max(0, search_range // 2 - 1) |
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# Looking only within the search range, |
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# count and flag the matched pairs. |
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num_com = 0 |
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yl1 = lent - 1 |
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for i in range(lens): |
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low_lim = (i - search_range) if (i >= search_range) else 0 |
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hi_lim = (i + search_range) if ((i + search_range) <= yl1) else yl1 |
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for j in range(low_lim, hi_lim + 1): |
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if (tar_flag[j] == 0) and (tar[j] == src[i]): |
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tar_flag[j] = 1 |
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src_flag[i] = 1 |
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num_com += 1 |
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break |
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# If no characters in common - return |
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if num_com == 0: |
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return 0.0 |
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# Count the number of transpositions |
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k = n_trans = 0 |
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for i in range(lens): |
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if src_flag[i] != 0: |
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j = 0 |
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for j in range(k, lent): # pragma: no branch |
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if tar_flag[j] != 0: |
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k = j + 1 |
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break |
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if src[i] != tar[j]: |
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n_trans += 1 |
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n_trans //= 2 |
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# Main weight computation for Jaro distance |
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weight = ( |
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num_com / lens + num_com / lent + (num_com - n_trans) / num_com |
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) |
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weight /= 3.0 |
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# Continue to boost the weight if the strings are similar |
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# This is the Winkler portion of Jaro-Winkler distance |
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if self._mode == 'winkler' and weight > self._boost_threshold: |
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# Adjust for having up to the first 4 characters in common |
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j = 4 if (minv >= 4) else minv |
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i = 0 |
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while (i < j) and (src[i] == tar[i]): |
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i += 1 |
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weight += i * self._scaling_factor * (1.0 - weight) |
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# Optionally adjust for long strings. |
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# After agreeing beginning chars, at least two more must agree and |
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# the agreeing characters must be > .5 of remaining characters. |
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if ( |
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self._long_strings |
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and (minv > 4) |
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and (num_com > i + 1) |
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and (2 * num_com >= minv + i) |
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): |
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weight += (1.0 - weight) * ( |
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(num_com - i - 1) / (lens + lent - i * 2 + 2) |
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) |
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return weight |
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if __name__ == '__main__': |
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import doctest |
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doctest.testmod() |
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chrislit /
abydos
