FuzzyWuzzy
- 簡單比較
>>> 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
- 單詞排序比
>>> fuzz.ratio("fuzzy wuzzy was ","wuzzy,fuzzy as dfd")
53
>>> fuzz.token_sort_ratio("fuzzy wuzzy was ","wuzzy,fuzzy as dfd")
67
- 單詞集合比
>>> 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)
計算漢明距離，要求str1很str2的長度必須一致。是描述兩個等長字串之間對應位置上不同字符的個數
- Levenshtein.distance(str1,str2)
計算編輯距離(也稱為Levenshtein距離)。是描述由一個字符轉化為另一個字符串最少的操作次數，在其中包括插入、刪除、替換
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)
> 計算萊文斯坦比。計算公式
```math
(sum - idist)/sum
其中sum是指str1和str2的字符串長度總和。idist是類編輯距離
注：這里的類編輯距離不是2中所講的編輯距離，2中三種操作中的每個操作+1，而此處，刪除、插入依然加+1，但是替換加2
這樣做的目的是：ratio('a','c'),sum=2 按2中計算為(2-1)/2=0.5,'a'/'c'沒有重合，顯然不合算，但是替換操作+2，就會解決這個問題
- Levenshtein.jaro(str1,str2)
計算jaro距離
其中m為s1,s2的匹配長度，當某位置的認為匹配當該位置字符相同，或者不超過
t是調換次數的一般
- Levenshtein.jaro_winkler(str1,str2)
計算jaro_Winkler距離
