问题描述
我想知道如何修改Damerau-Levenshtein算法以跟踪将源字符串更改为目标字符串所需的特定字符转换.已为 Levenshtein距离，但我找不到DL距离的任何答案.
I'm wondering how to modify the Damerau-Levenshtein algorithm to track the specific character transformations required to change a source string to a target string. This question has been answered for the Levenshtein distance, but I couldn't find any answers for DL distance.
我查看了 py-Levenshtein 模块:它提供了我所需要的，但是对于Levenshtein距离:
I looked at the py-Levenshtein module: it provides exactly what I need, but for Levenshtein distance:
Levenshtein.editops("FBBDE", "BCDASD")
[('delete', 0, 0), ('replace', 2, 1), ('insert', 4, 3), ('insert', 4,
4), ('replace', 4, 5)]
由于editops的代码是用C语言编写的，因此很难解密.我想知道如何高效地进行跟踪转换:我想像是通过距离矩阵来实现的，它看起来像:
The code for editops was difficult to decipher since it's written in C. I wonder how tracking transformations the can be done efficiently: I imagine it is possible from the distance matrix, which looks something like:
r e p u b l i c a n
0 1 2 3 4 5 6 7 8 9 10
d 1 1 2 3 4 5 6 7 8 9 10
e 2 2 1 2 3 4 5 6 7 8 9
m 3 3 2 2 3 4 5 6 7 8 9
o 4 4 3 3 3 4 5 6 7 8 9
c 5 5 4 4 4 4 5 6 6 7 8
r 6 5 5 5 5 5 5 6 7 7 8
a 7 6 6 6 6 6 6 6 7 7 8
t 8 7 7 7 7 7 7 7 7 8 8
推荐答案
import numpy as np
def levenshtein_distance(string1, string2):
n1 = len(string1)
n2 = len(string2)
return _levenshtein_distance_matrix(string1, string2)[n1, n2]
def damerau_levenshtein_distance(string1, string2):
n1 = len(string1)
n2 = len(string2)
return _levenshtein_distance_matrix(string1, string2, True)[n1, n2]
def get_ops(string1, string2, is_damerau=False):
i, j = _levenshtein_distance_matrix(string1, string2, is_damerau).shape
i -= 1
j -= 1
ops = list()
while i != -1 and j != -1:
if is_damerau:
if i > 1 and j > 1 and string1[i-1] == string2[j-2] and string1[i-2] == string2[j-1]:
if dist_matrix[i-2, j-2] < dist_matrix[i, j]:
ops.insert(0, ('transpose', i - 1, i - 2))
i -= 2
j -= 2
continue
index = np.argmin([dist_matrix[i-1, j-1], dist_matrix[i, j-1], dist_matrix[i-1, j]])
if index == 0:
if dist_matrix[i, j] > dist_matrix[i-1, j-1]:
ops.insert(0, ('replace', i - 1, j - 1))
i -= 1
j -= 1
elif index == 1:
ops.insert(0, ('insert', i - 1, j - 1))
j -= 1
elif index == 2:
ops.insert(0, ('delete', i - 1, i - 1))
i -= 1
return ops
def execute_ops(ops, string1, string2):
strings = [string1]
string = list(string1)
shift = 0
for op in ops:
i, j = op[1], op[2]
if op[0] == 'delete':
del string[i + shift]
shift -= 1
elif op[0] == 'insert':
string.insert(i + shift + 1, string2[j])
shift += 1
elif op[0] == 'replace':
string[i + shift] = string2[j]
elif op[0] == 'transpose':
string[i + shift], string[j + shift] = string[j + shift], string[i + shift]
strings.append(''.join(string))
return strings
def _levenshtein_distance_matrix(string1, string2, is_damerau=False):
n1 = len(string1)
n2 = len(string2)
d = np.zeros((n1 + 1, n2 + 1), dtype=int)
for i in range(n1 + 1):
d[i, 0] = i
for j in range(n2 + 1):
d[0, j] = j
for i in range(n1):
for j in range(n2):
if string1[i] == string2[j]:
cost = 0
else:
cost = 1
d[i+1, j+1] = min(d[i, j+1] + 1, # insert
d[i+1, j] + 1, # delete
d[i, j] + cost) # replace
if is_damerau:
if i > 0 and j > 0 and string1[i] == string2[j-1] and string1[i-1] == string2[j]:
d[i+1, j+1] = min(d[i+1, j+1], d[i-1, j-1] + cost) # transpose
return d
if __name__ == "__main__":
# GIFTS PROFIT
# FBBDE BCDASD
# SPARTAN PART
# PLASMA ALTRUISM
# REPUBLICAN DEMOCRAT
# PLASMA PLASMA
# FISH IFSH
# STAES STATES
string1 = 'FISH'
string2 = 'IFSH'
for is_damerau in [True, False]:
if is_damerau:
print('=== damerau_levenshtein_distance ===')
else:
print('=== levenshtein_distance ===')
dist_matrix = _levenshtein_distance_matrix(string1, string2, is_damerau=is_damerau)
print(dist_matrix)
ops = get_ops(string1, string2, is_damerau=is_damerau)
print(ops)
res = execute_ops(ops, string1, string2)
print(res)
输出:
=== damerau_levenshtein_distance ===
[[0 1 2 3 4]
[1 1 1 2 3]
[2 1 1 2 3]
[3 2 2 1 2]
[4 3 3 2 1]]
[('transpose', 1, 0)]
['FISH', 'IFSH']
=== levenshtein_distance ===
[[0 1 2 3 4]
[1 1 1 2 3]
[2 1 2 2 3]
[3 2 2 2 3]
[4 3 3 3 2]]
[('replace', 0, 0), ('replace', 1, 1)]
['FISH', 'IISH', 'IFSH']
这篇关于修改Damerau-Levenshtein算法以跟踪转换(插入，删除等)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！