Search results
Results from the WOW.Com Content Network
A classic example of a problem which a regular grammar cannot handle is the question of whether a given string contains correctly nested parentheses. (This is typically handled by a Chomsky Type 2 grammar, also termed a context-free grammar .)
P denotes the string to be searched for, called the pattern. Its length is m. S[i] denotes the character at index i of string S, counting from 1. S[i..j] denotes the substring of string S starting at index i and ending at j, inclusive. A prefix of S is a substring S[1..i] for some i in range [1, l], where l is the length of S.
The (standard) Boolean model of information retrieval (BIR) [1] is a classical information retrieval (IR) model and, at the same time, the first and most-adopted one. [2] The BIR is based on Boolean logic and classical set theory in that both the documents to be searched and the user's query are conceived as sets of terms (a bag-of-words model ).
However, there can be many ways to write a regular expression for the same set of strings: for example, (Hän|Han|Haen)del also specifies the same set of three strings in this example. Most formalisms provide the following operations to construct regular expressions. Boolean "or" A vertical bar separates alternatives.
(search substring string) Common Lisp: returns NIL (string-index substring string) ISLISP: returns nil: List.findIndex (List.isPrefixOf substring) (List.tails string) Haskell (returns only index) returns Nothing Str.search_forward (Str.regexp_string substring) string 0: OCaml: raises Not_found
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.
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
Alternatively, ternary search trees are effective when storing a large number of relatively short strings (such as words in a dictionary). [1] Running times for ternary search trees are similar to binary search trees, in that they typically run in logarithmic time, but can run in linear time in the degenerate (worst) case. Further, the size of ...