Python implementation of string matching algorithm example code

This article mainly introduces the code examples of string matching algorithm implemented in Python, involving problems in string matching, brute force string matching, and Horspool algorithm. It has certain reference value. Friends who need it can learn about it.

Problems with string matching

There are two ways to find whether a substring exists in a long string in Python: 1. It is the find() function of str. The find() function only returns the starting position of the substring matched. If not, it returns -1; the second is the findall function of the re module, which can return all matched substrings.

But if you use the findall function, you need to pay attention to the special characters that exist in the string.

Brute force string matching:

Align the pattern to the first m (pattern length) characters of the text, then from left to Right matches each pair of corresponding characters until all are matched or a non-matching character is encountered. In the latter case, the pattern is shifted one position to the right.

The code is as follows:

def string_match(string, sub_str): # 蛮力法字符串匹配 for i in range(len(string)-len(sub_str)+1): index = i # index指向下一个待比较的字符 for j in range(len(sub_str)): if string[index] == sub_str[j]: index += 1 else: break if index-i == len(sub_str): return i return -1 if __name__ == "__main__": print(string_match("adbcbdc", "dc"))

In the worst case, the algorithm belongs to Θ(nm), in fact, the average efficiency ratio of the algorithm The worst efficiency is much better. In fact, when searching for random text, its efficiency is linear Θ(n).

Horspool algorithm:

The Horsepool algorithm is a simplified version of the Boyer-Moore algorithm, which is also a typical example of exchanging space for time. The algorithm aligns the beginning characters of pattern P and text T, starting the comparison from the last character of the pattern. If the attempted comparison fails, it moves the pattern backward. The comparison is from right to left during each trial.

In the brute force algorithm, each movement of the pattern is a character. The core idea of the Horspool algorithm is to use space in exchange for time to increase the movement range of the pattern matching window. Different from the brute force algorithm, the pattern matching is from right to left, and the distance of each movement is calculated in advance and stored in the table.

The code is as follows:

__author__ = 'Wang' from collections import defaultdict def shift_table(pattern): # 生成 Horspool 算法的移动表 # 当前检测字符为c，模式长度为m # 如果当前c不包含在模式的前m-1个字符中，移动模式的长度m # 其他情况下移动最右边的的c到模式最后一个字符的距离 table = defaultdict(lambda: len(pattern)) for index in range(0, len(pattern)-1): table[pattern[index]] = len(pattern) - 1 - index return table def horspool_match(pattern, text): # 实现 horspool 字符串匹配算法 # 匹配成功，返回模式在text中的开始部分；否则返回 -1 table = shift_table(pattern) index = len(pattern) - 1 while index <= len(text) - 1: print("start matching at", index) match_count = 0 while match_count < len(pattern) and pattern[len(pattern)-1-match_count] == text[index-match_count]: match_count += 1 if match_count == len(pattern): return index-match_count+1 else: index += table[text[index]] return -1 if __name__ == "__main__": print(horspool_match("barber", "jim_saw_me_in_a_barbershopp"))

Obviously, the worst efficiency of the Horspool algorithm belongs to Θ(nm). When searching for random text, its efficiency is linear Θ(n). Although the efficiency type is the same, on average, the Horspool algorithm is much faster than the brute force algorithm.

The above content is the example code of string matching algorithm implemented in Python. I hope it can help everyone.

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