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Besides the built-in RE/flex POSIX regex pattern matcher, RE/flex also supports PCRE2, Boost.Regex and std::regex pattern matching libraries. PCRE2 and Boost.Regex offer a richer regular expression pattern syntax with Perl pattern matching semantics, but are slower due to their intrinsic NFA-based matching algorithm.
Python: python.org: Python Software Foundation License: Python has two major implementations, the built in re and the regex library. Ruby: ruby-doc.org: GNU Library General Public License: Ruby 1.8, Ruby 1.9, and Ruby 2.0 and later versions use different engines; Ruby 1.9 integrates Oniguruma, Ruby 2.0 and later integrate Onigmo, a fork from ...
If the portion of the regular expression is "greedy", it will match as many characters as possible. If it is not greedy, it will match as few characters as possible. By default, quantifiers in AWB are greedy. To make a quantifier non-greedy, it must be followed by a question mark. For example: In this string:
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
Self-validation: [19] re2c has a special mode in which it ignores all used-defined interface code and generates a self-contained skeleton program. Additionally, re2c generates two files: one with the input strings derived from the regular grammar, and one with compressed match results that are used to verify lexer behavior on all inputs.
Common applications of approximate matching include spell checking. [5] With the availability of large amounts of DNA data, matching of nucleotide sequences has become an important application. [1] Approximate matching is also used in spam filtering. [5] Record linkage is a common application where records from two disparate databases are matched.
Gestalt pattern matching, [1] also Ratcliff/Obershelp pattern recognition, [2] is a string-matching algorithm for determining the similarity of two strings. It was developed in 1983 by John W. Ratcliff and John A. Obershelp and published in the Dr. Dobb's Journal in July 1988.
The participants on one side of the matching (the hospitals) may have a numerical capacity, specifying the number of doctors they are willing to hire. The total number of participants on one side might not equal the total capacity to which they are to be matched on the other side. The resulting matching might not match all of the participants.