模糊搜索代码
时间: 2023-07-23 07:07:54 浏览: 57
模糊搜索代码可以基于模糊匹配算法实现,比如常见的Levenshtein距离算法。以下是一个Python的例子:
```python
import numpy as np
def levenshtein_distance(s1, s2):
m = len(s1)
n = len(s2)
dp = np.zeros((m+1, n+1))
for i in range(m+1):
dp[i][0] = i
for j in range(n+1):
dp[0][j] = j
for i in range(1, m+1):
for j in range(1, n+1):
if s1[i-1] == s2[j-1]:
dp[i][j] = dp[i-1][j-1]
else:
dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1])
return dp[m][n]
def fuzzy_search(query, candidates, threshold):
results = []
for candidate in candidates:
distance = levenshtein_distance(query, candidate)
if distance <= threshold:
results.append(candidate)
return results
```
其中,`levenshtein_distance`计算两个字符串之间的Levenshtein距离,`fuzzy_search`用于对query进行模糊搜索,返回与候选字符串的距离小于等于阈值threshold的结果。
例如:
```python
candidates = ['apple', 'banana', 'orange', 'peach', 'pear']
query = 'app'
threshold = 2
results = fuzzy_search(query, candidates, threshold)
print(results)
```
输出:
```python
['apple']
```
这里的结果是'apple',因为'apple'和'app'之间的距离为1,小于等于阈值2。