Boyer–Moore–Horspool Algorithm
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In
computer science Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, the Boyer–Moore–Horspool algorithm or Horspool's algorithm is an
algorithm In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
for finding
substring In formal language theory and computer science, a substring is a contiguous sequence of characters within a string. For instance, "''the best of''" is a substring of "''It was the best of times''". In contrast, "''Itwastimes''" is a subsequenc ...
s in strings. It was published by Nigel Horspool in 1980 as SBM. It is a simplification of the
Boyer–Moore string-search algorithm In computer science, the Boyer–Moore string-search algorithm is an efficient string-searching algorithm that is the standard benchmark for practical string-search literature. It was developed by Robert S. Boyer and J Strother Moore in 1977. ...
which is related to the Knuth–Morris–Pratt algorithm. The algorithm trades space for time in order to obtain an
average-case complexity In computational complexity theory, the average-case complexity of an algorithm is the amount of some computational resource (typically time) used by the algorithm, averaged over all possible inputs. It is frequently contrasted with worst-case comp ...
of ''O(n)'' on random text, although it has ''O(nm)'' in the
worst case In computer science, best, worst, and average cases of a given algorithm express what the resource usage is ''at least'', ''at most'' and ''on average'', respectively. Usually the resource being considered is running time, i.e. time complexity, b ...
, where the length of the pattern is ''m'' and the length of the search string is ''n''.


Description

Like Boyer–Moore, Boyer–Moore–Horspool preprocesses the pattern to produce a table containing, for each symbol in the
alphabet An alphabet is a standard set of letter (alphabet), letters written to represent particular sounds in a spoken language. Specifically, letters largely correspond to phonemes as the smallest sound segments that can distinguish one word from a ...
, the number of characters that can safely be skipped. The preprocessing phase, in pseudocode, is as follows (for an alphabet of 256 symbols, i.e., bytes): := length(pattern) for i from 0 to length(pattern) - 1 exclusive T attern[i := length(pattern) - 1 - i return T Pattern search proceeds as follows. The procedure reports the index of the first occurrence of in . // Compares two strings, up to the first len characters. // Note: this is equivalent to !memcmp(str1, str2, len). function same(str1, str2, len) i := len - 1 // The original algorithm tries to play smart here: it checks for the // last character, then second-last, etc. while str1

str2 if i

0 return true i := i - 1 return false function search(needle, haystack) T := preprocess(needle) skip := 0 // haystack[skip:] means substring starting at index `skip`. Would be &haystack kipin C. while length(haystack) - skip >= length(needle) if same(haystack kip: needle, length(needle)) return skip skip := skip + T aystack[skip + length(needle) - 1 return -1


Performance

The algorithm performs best with long needle strings, when it consistently hits a non-matching character at or near the final byte of the current position in the haystack and the final byte of the needle does not occur elsewhere within the needle. For instance a 32 byte needle ending in "z" searching through a 255 byte haystack which does not have a 'z' byte in it would take up to 224 byte comparisons. The best case is the same as for the Boyer–Moore string-search algorithm in big O notation, although the constant overhead of initialization and for each loop is less. The worst case behavior happens when the bad character skip is consistently low (with the lower limit of 1 byte movement) and a large portion of the needle matches the haystack. The bad character skip is only low, on a partial match, when the final character of the needle also occurs elsewhere within the needle, with 1 byte movement happening when the same byte is in both of the last two positions. The canonical degenerate case similar to the above "best" case is a needle of an 'a' byte followed by 31 'z' bytes in a haystack consisting of 255 'z' bytes. This will do 31 successful byte comparisons, a 1 byte comparison that fails and then move forward 1 byte. This process will repeat 223 more times (255 − 32), bringing the total byte comparisons to 7,168 (32 × 224). (A different byte-comparison loop will have a different behavior.) The worst case is significantly higher than for the Boyer–Moore string-search algorithm, although obviously this is hard to achieve in normal use cases. It is also worth noting that this worst case is also the worst case for the naive (but usual) memcmp() algorithm, although the implementation of that tends to be significantly optimized (and is more cache friendly).


Tuning the comparison loop

The original algorithm had a more sophisticated same() loop. It uses an extra pre-check before proceeding in the positive direction: function same_orig(str1, str2, len) i ← 0 if str1 en - 1= str2 en - 1 while str1 = str2 if i = len - 2 return true i ← i + 1 return false A tuned version of the BMH algorithm is the Raita algorithm. It adds an additional precheck for the middle character, in the order of last-first-middle. The algorithm enters the full loop only when the check passes: function same_raita(str1, str2, len) i ← 0 mid ← len / 2 ''Three prechecks.'' if len ≥ 3 if str id!= str2 id return false if len ≥ 1 if str != str2 return false if len ≥ 2 if str en - 1!= str2 en - 1 return false ''Any old comparison loop.'' return len < 3 ''or'' SAME(&str1 &str2 len - 2) It is unclear whether this 1992 tuning still holds its performance advantage on modern machines. The rationale by the authors is that actual text usually contains some patterns which can be effectively prefiltered by these three characters. It appears that Raita is not aware of the old last-character precheck (he believed that the backward-only routine is the Horspool implementation), so readers are advised to take the results with a grain of salt. On modern machines, library functions like tends to provide better throughput than any of the hand-written comparison loops. The behavior of an "SFC" loop (Horspool's terminology) both in libstdc++ and libc++ seems to suggest that a modern Raita implementation should not include any of the one-character shifts, since they have detrimental effects on data alignment. Also see String-searching algorithm which has detailed analysis of other string searching algorithms.


References


External links


Description of the algorithm

An implementation from V8 JavaScript engine written in C++
{{DEFAULTSORT:Boyer-Moore-Horspool algorithm String matching algorithms Articles with example C code