Python实现计算最小编辑距离

 def normal_leven(str1, str2):
   len_str1 = len(str1) + 1
   len_str2 = len(str2) + 1
   #create matrix
   matrix = [0 for n in range(len_str1 * len_str2)]
   #init x axis
   for i in range(len_str1):
     matrix[i] = i
   #init y axis
   for j in range(0, len(matrix), len_str1):
     if j % len_str1 == 0:
       matrix[j] = j // len_str1

   for i in range(1, len_str1):
     for j in range(1, len_str2):
       if str1[i-1] == str2[j-1]:
         cost = 0
       else:
         cost = 1
       matrix[j*len_str1+i] = min(matrix[(j-1)*len_str1+i]+1,
                     matrix[j*len_str1+(i-1)]+1,
                     matrix[(j-1)*len_str1+(i-1)] + cost)

   return matrix[-1]

 def difflib_leven(str1, str2):
  leven_cost = 0
  s = difflib.SequenceMatcher(None, str1, str2)
  for tag, i1, i2, j1, j2 in s.get_opcodes():
    #print('{:7} a[{}: {}] --> b[{}: {}] {} --> {}'.format(tag, i1, i2, j1, j2, str1[i1: i2], str2[j1: j2]))

    if tag == 'replace':
      leven_cost += max(i2-i1, j2-j1)
    elif tag == 'insert':
      leven_cost += (j2-j1)
    elif tag == 'delete':
      leven_cost += (i2-i1)
  return leven_cost