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In order to find the number of occurrences of a given string (length ) in a text (length ), [3] We use binary search against the suffix array of to find the starting and end position of all occurrences of .
The problem of finding the longest substring with at least occurrences can be solved by first preprocessing the tree to count the number of leaf descendants for each internal node, and then finding the deepest node with at least leaf descendants. To avoid overlapping repeats, you can check that the list of suffix lengths has no consecutive ...
The Boyer–Moore algorithm searches for occurrences of P in T by performing explicit character comparisons at different alignments. Instead of a brute-force search of all alignments (of which there are n − m + 1 {\displaystyle n-m+1} ), Boyer–Moore uses information gained by preprocessing P to skip as many alignments as possible.
Here input is the input array to be sorted, key returns the numeric key of each item in the input array, count is an auxiliary array used first to store the numbers of items with each key, and then (after the second loop) to store the positions where items with each key should be placed, k is the maximum value of the non-negative key values and ...
The Boyer–Moore majority vote algorithm is an algorithm for finding the majority of a sequence of elements using linear time and a constant number of words of memory. It is named after Robert S. Boyer and J Strother Moore , who published it in 1981, [ 1 ] and is a prototypical example of a streaming algorithm .
Generalized suffix arrays can be used to solve the pattern matching problem: [5]. Given a pattern and a text , find all occurrences of in .; Using the generalized suffix array of , then first, the suffixes that have as a prefix need to be found.
A possible implementation of t is as a set of pairs of the form (v i, c i) where each v i is a distinct value in t and c i is the number of occurrences of v i in t. Then d is the size of this set. The step "Delete k distinct values from t" amounts to reducing each c i by 1 and then removing any pair (v i, c i) from the set if c i becomes 0.
Apostolico–Giancarlo speeds this up by recording the number of characters matched at the alignments of in a table, which is combined with data gathered during the pre-processing of to avoid redundant equality checking for sequences of characters that are known to match.