FuzzyWuzzy
- 简单比较
>>> from fuzzywuzzy import fuzz
>>> fuzz.ratio("this is pass","this is a poce!")
67
- 部分比
>>> fuzz.partial_ratio("this is a text", "this is a test!")
93
- 单词排序比
>>> fuzz.ratio("fuzzy wuzzy was ","wuzzy,fuzzy as dfd")
53
>>> fuzz.token_sort_ratio("fuzzy wuzzy was ","wuzzy,fuzzy as dfd")
67
- 单词集合比
>>> fuzz.token_sort_ratio("fuzzy was a ced", "fuzzy fuzzy wer a bear")
59
>>> fuzz.token_set_ratio("fuzzy was a ced", "fuzzy fuzzy wer a bear")
71
- Process
>>> from suzzywuzzy import process
>>> choices = ["Atlanta hello", "New York Jets", "New York Giants", "Dallas bob_dd"]
>>> process.extract("new york jets", choices, limit=2)
[('New York Jets', 100), ('New York Giants', 79)]
>>> process.extractOne("cowboys", choices)
("Dallas Cowboys", 90)
Levenshtein
- Levenshtein.hamming(str1,str2)
计算汉明距离，要求str1很str2的长度必须一致。是描述两个等长字串之间对应位置上不同字符的个数
- Levenshtein.distance(str1,str2)
计算编辑距离(也称为Levenshtein距离)。是描述由一个字符转化为另一个字符串最少的操作次数，在其中包括插入、删除、替换
def levenshtein(first,second):
if len(first)>len(second):
first,second = second,first
if len(first) == 0:
return len(second)
if len(second) == 0:
return len(first)
first_length = len(first)+1
second_length = len(second)+1
distance_matrix = [range(second_length) for x in range(first_length)]
print distance_matrix[1][1],distance_matrix[1][2],distance_matrix[1][3],distance_matrix[1][4]
for i in range(1,first_length):
for j in range(1,second_length):
deletion = distance_matrix[i-1][j]+1
insertion = distance_matrix[i][j-1]+1
substitution = distance_matrix[i-1][j-1]
if first[i-1] != second[j-1]:
substitution += 1
distance_matrix[i][j] = min(insertion,deletion,substitution)
print distan_matrix
return distance_matrix[first_length-1][second_length-1]
```
- Levenshtein.ratio(str1,str2)
> 计算莱文斯坦比。计算公式
```math
(sum - idist)/sum
其中sum是指str1和str2的字符串长度总和。idist是类编辑距离
注：这里的类编辑距离不是2中所讲的编辑距离，2中三种操作中的每个操作+1，而此处，删除、插入依然加+1，但是替换加2
这样做的目的是：ratio('a','c'),sum=2 按2中计算为(2-1)/2=0.5,'a'/'c'没有重合，显然不合算，但是替换操作+2，就会解决这个问题
- Levenshtein.jaro(str1,str2)
计算jaro距离
其中m为s1,s2的匹配长度，当某位置的认为匹配当该位置字符相同，或者不超过
t是调换次数的一般
- Levenshtein.jaro_winkler(str1,str2)
计算jaro_Winkler距离
