psm 各种匹配方法的代码

PSM（Probabilistic Sequence Matching）是一种常用于序列匹配的概率模型，可以用于文本相似度计算、信息抽取、语音识别等领域。下面是几种PSM的匹配方法的代码示例：

1. Hamming Distance（汉明距离）匹配方法：

def hamming_distance(str1, str2):
    distance = 0
    for i in range(len(str1)):
        if str1[i] != str2[i]:
            distance += 1
    return distance

str1 = "hello"
str2 = "hallo"
distance = hamming_distance(str1, str2)
print("Hamming Distance:", distance)

2. Levenshtein Distance（编辑距离）匹配方法：

def levenshtein_distance(str1, str2):
    m = len(str1) + 1
    n = len(str2) + 1
    dp = [[0] * n for _ in range(m)]
    for i in range(m):
        dp[i][0] = i
    for j in range(n):
        dp[0][j] = j
    for i in range(1, m):
        for j in range(1, n):
            cost = 0 if str1[i-1] == str2[j-1] else 1
            dp[i][j] = min(dp[i-1][j] + 1, dp[i][j-1] + 1, dp[i-1][j-1] + cost)
    return dp[m-1][n-1]

str1 = "kitten"
str2 = "sitting"
distance = levenshtein_distance(str1, str2)
print("Levenshtein Distance:", distance)

3. Jaccard Similarity（杰卡德相似度）匹配方法：

def jaccard_similarity(set1, set2):
    intersection = len(set1.intersection(set2))
    union = len(set1.union(set2))
    similarity = intersection / union
    return similarity

set1 = {'apple', 'banana', 'orange'}
set2 = {'banana', 'grape', 'mango'}
similarity = jaccard_similarity(set1, set2)
print("Jaccard Similarity:", similarity)

这些是PSM常用的几种匹配方法的代码示例，根据具体的匹配需求选择适合的方法进行使用。