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Source Code for Module translate.search.lshtein

  1  #!/usr/bin/env python 
  2  # -*- coding: utf-8 -*- 
  3  # 
  4  # Copyright 2006-2009 Zuza Software Foundation 
  5  # 
  6  # This file is part of translate. 
  7  # 
  8  # This program is free software; you can redistribute it and/or modify 
  9  # it under the terms of the GNU General Public License as published by 
 10  # the Free Software Foundation; either version 2 of the License, or 
 11  # (at your option) any later version. 
 12  # 
 13  # This program is distributed in the hope that it will be useful, 
 14  # but WITHOUT ANY WARRANTY; without even the implied warranty of 
 15  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the 
 16  # GNU General Public License for more details. 
 17  # 
 18  # You should have received a copy of the GNU General Public License 
 19  # along with this program; if not, see <http://www.gnu.org/licenses/>. 
 20   
 21  """A class to calculate a similarity based on the Levenshtein  
 22  distance. See http://en.wikipedia.org/wiki/Levenshtein_distance. 
 23   
 24  If available, the python-Levenshtein package will be used which will provide 
 25  better performance as it is implemented natively. See 
 26  http://trific.ath.cx/python/levenshtein/ 
 27  """ 
 28   
 29  import math 
 30  import sys 
 31   
32 -def python_distance(a, b, stopvalue=-1):
33 """Calculates the distance for use in similarity calculation. Python 34 version.""" 35 l1 = len(a) 36 l2 = len(b) 37 if stopvalue == -1: 38 stopvalue = l2 39 current = range(l1+1) 40 for i in range(1, l2+1): 41 previous, current = current, [i]+[0]*l1 42 least = l2 43 for j in range(1, l1 + 1): 44 change = previous[j-1] 45 if a[j-1] != b[i-1]: 46 change = change + 1 47 insert = previous[j] + 1 48 delete = current[j-1] + 1 49 current[j] = min(insert, delete, change) 50 if least > current[j]: 51 least = current[j] 52 #The smallest value in the current array is the best (lowest) value 53 #that can be attained in the end if the strings are identical further 54 if least > stopvalue: 55 return least 56 57 return current[l1]
58
59 -def native_distance(a, b, stopvalue=0):
60 """Same as python_distance in functionality. This uses the fast C 61 version if we detected it earlier. 62 63 Note that this does not support arbitrary sequence types, but only 64 string types.""" 65 return Levenshtein.distance(a, b)
66 67 try: 68 import Levenshtein as Levenshtein 69 distance = native_distance 70 except Exception: 71 import logging 72 logging.warning("Python-Levenshtein not found. Continuing with built-in (slower) fuzzy matching.") 73 distance = python_distance 74
75 -class LevenshteinComparer:
76 - def __init__(self, max_len=200):
77 self.MAX_LEN = max_len
78
79 - def similarity(self, a, b, stoppercentage=40):
80 similarity = self.similarity_real(a, b, stoppercentage) 81 measurements = 1 82 83 # chr_a = segment.characters(a) 84 # chr_b = segment.characters(b) 85 # if chr_a and chr_b and abs(len(chr_a) - len(a)) + abs(len(chr_b) - len(b)): 86 # similarity += self.similarity_real(chr_a, chr_b, stoppercentage) 87 # measurements += 1 88 # else: 89 # similarity *= 2 90 # measurements += 1 91 # 92 # wrd_a = segment.words(a) 93 # wrd_b = segment.words(b) 94 # if len(wrd_a) + len(wrd_b) > 2: 95 # similarity += self.similarity_real(wrd_a, wrd_b, 0) 96 # measurements += 1 97 return similarity / measurements
98
99 - def similarity_real(self, a, b, stoppercentage=40):
100 """Returns the similarity between a and b based on Levenshtein distance. It 101 can stop prematurely as soon as it sees that a and b will be no simmilar than 102 the percentage specified in stoppercentage. 103 104 The Levenshtein distance is calculated, but the following should be noted: 105 - Only the first MAX_LEN characters are considered. Long strings differing 106 at the end will therefore seem to match better than they should. See the 107 use of the variable penalty to lessen the effect of this. 108 - Strings with widely different lengths give the opportunity for shortcut. 109 This is by definition of the Levenshtein distance: the distance will be 110 at least as much as the difference in string length. 111 - Calculation is stopped as soon as a similarity of stoppercentage becomes 112 unattainable. See the use of the variable stopvalue. 113 - Implementation uses memory O(min(len(a), len(b)) 114 - Excecution time is O(len(a)*len(b)) 115 """ 116 l1, l2 = len(a), len(b) 117 if l1 == 0 or l2 == 0: 118 return 0 119 #Let's make l1 the smallest 120 if l1 > l2: 121 l1, l2 = l2, l1 122 a, b = b, a 123 124 #maxsimilarity is the maximum similarity that can be attained as constrained 125 #by the difference in string length 126 maxsimilarity = 100 - 100.0*abs(l1 - l2)/l2 127 if maxsimilarity < stoppercentage: 128 return maxsimilarity * 1.0 129 130 #Let's penalise the score in cases where we shorten strings 131 penalty = 0 132 if l2 > self.MAX_LEN: 133 b = b[:self.MAX_LEN] 134 l2 = self.MAX_LEN 135 penalty += 7 136 if l1 > self.MAX_LEN: 137 a = a[:self.MAX_LEN] 138 l1 = self.MAX_LEN 139 penalty += 7 140 141 #The actual value in the array that would represent a giveup situation: 142 stopvalue = math.ceil((100.0 - stoppercentage)/100 * l2) 143 dist = distance(a, b, stopvalue) 144 if dist > stopvalue: 145 return stoppercentage - 1.0 146 147 #If MAX_LEN came into play, we consider the calculated distance to be 148 #representative of the distance between the whole, untrimmed strings 149 if dist != 0: 150 penalty = 0 151 return 100 - (dist*1.0/l2)*100 - penalty
152 153 154 if __name__ == "__main__": 155 from sys import argv 156 comparer = LevenshteinComparer() 157 print "Similarity:\n%s" % comparer.similarity(argv[1], argv[2], 50) 158