#StackBounty: #python #performance #algorithm #strings #search Naive implementation of KMP algorithm

Bounty: 50

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 kmp_table and kmp_search.

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 [1]: text = "A"*1000000 + "B"
In [2]: word = "A"*100 + "B"
In [3]: %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 ┬Ás 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 = [0] * len(word)
    position, candidate = 2, 0
    table[0] = -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)


Get this bounty!!!