After reading this answer to the question "High execution time to count overlapping substrings", I decided to implement the suggested Knuth-Morris-Pratt (KMP) algorithm. I used the pseudo-code listed on Wikipedia for the functions
However, when running it on some corner-cases, I have observed that it is a lot slower than the standard
str.find, which apparently uses a modified Boyer-Moore-Horspool algorithm and should thus have worse worst-case performance.
The specific case I looked at is:
$ ipython -i kmp.py In : text = "A"*1000000 + "B" In : word = "A"*100 + "B" In : %timeit kmp_search(text, word) 1 loop, best of 3: 410 ms per loop In [4}: %timeit text.find(word) 1000 loops, best of 3: 703 µs per loop
So the difference is about a factor 1000 for this input. This is probably due to the fact that the native one is written in C and this is written in Python, but I still wanted to see if I did anything stupid here or missed any obvious optimization.
def kmp_table(word): table =  * len(word) position, candidate = 2, 0 table = -1 while position < len(word): if word[position - 1] == word[candidate]: table[position] = candidate + 1 candidate += 1 position += 1 elif candidate > 0: candidate = table[candidate] else: table[position] = 0 position += 1 return table def kmp_search(text, word): m, i = 0, 0 table = kmp_table(word) while m + i < len(text): if word[i] == text[m + i]: if i == len(word) - 1: return m i += 1 else: if table[i] > -1: m += i - table[i] i = table[i] else: m += 1 i = 0 return len(text)