Pearson perfect hashing

I'm trying to write a开发者_开发技巧 generator that produces Pearson perfect hashes. Note that I don't need a minimal perfect hash. Wikipedia says that a Pearson perfect hash can be found in O(|S|) time using a randomized algorithm (where S is the set of keys). However, I haven't been able to find such an algorithm online. Is this even possible?

Note: I don't want to use gperf/cmph/etc., I'd rather write my own implementation.

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Pearson's original paper outlines an algorithm to construct a permutation table T for perfect hashing:

The table T at the heart of this new hashing function can sometimes be modified to produce a minimal, perfect hashing function over a modest list of words. In fact, one can usually choose the exact value of the function for a particular word. For example, Knuth [3] illustrates perfect hashing with an algorithm that maps a list of 31 common English words onto unique integers between −10 and 30. The table T presented in Table II maps these same 31 words onto the integers from 1 to 31 in alphabetic order.

Although the procedure for constructing the table in Table II is too involved to be detailed here, the following highlights will enable the interested reader to repeat the process:

1. A table T was constructed by pseudorandom permutation of the integers (0 ... 255).
2. One by one, the desired values were assigned to the words in the list. Each assignment was effected by exchanging two elements in the table.
3. For each word, the first candidate considered for exchange was T[h[n − 1] ⊕ C[n]], the last table element referenced in the computation of the hash function for that word.
4. A table element could not be exchanged if it was referenced during the hashing of a previously assigned word or if it was referenced earlier in the hashing of the same word.
5. If the necessary exchange was forbidden by Rule 4, attention was shifted to the previously referenced table element, T[h[n − 2] ⊕ C[n − 1]].

The procedure is not always successful. For example, using the ASCII character codes, if the word “a” hashes to 0 and the word “i” hashes to 15, it turns out that the word “in” must hash to 0. Initial attempts to map Knuth's 31 words onto the integers (0 ... 30) failed for exactly this reason. The shift to the range (1 ... 31) was an ad hoc tactic to circumvent this problem.

Does this tampering with T damage the statistical behavior of the hashing function? Not seriously. When the 26,662 dictionary entries are hashed into 256 bins, the resulting distribution is still not significantly different from uniform (χ² = 266.03, 255 d.f., p = 0.30). Hashing the 128 randomly selected dictionary words resulted in an average of 27.5 collisions versus 26.8 with the unmodified T. When this function is extended as described above to produce 16-bit hash indices, the same test produces a substantially greater number of collisions (4,870 versus 4,721 with the unmodified T), although the distribution still is not significantly different from uniform (χ² = 565.2, 532 d.f., p = 0.154).