Python基于动态规划算法计算单词距离
本文实例讲述了Python基于动态规划算法计算单词距离。分享给大家供大家参考。具体如下：
#!/usr/bin/env python
#coding=utf-8
def word_distance(m,n):
"""compute the least steps number to convert m to n by insert , delete , replace .
动态规划算法,计算单词距离
>>> print word_distance("abc","abec")
1
>>> print word_distance("ababec","abc")
3
"""
len_1=lambda x:len(x)+1
c=[[i] for i in range(0,len_1(m)) ]
c[0]=[j for j in range(0,len_1(n))]
for i in range(0,len(m)):
# print i,' ',
for j in range(0,len(n)):
c[i+1].append(
min(
c[i][j+1]+1,#插入n[j]
c[i+1][j]+1,#删除m[j]
c[i][j] + (0 if m[i]==n[j] else 1 )#改
)
)
# print c[i+1][j+1],m[i],n[j],' ',
# print ''
return c[-1][-1]
import doctest
doctest.testmod()
raw_input("Success!")
希望本文所述对大家的Python程序设计有所帮助。
