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Regular Expression Flavor Comparison – Detailed comparison of the most popular regular expression flavors; Regexp Syntax Summary; Online Regular Expression Testing – with support for Java, JavaScript, .Net, PHP, Python and Ruby; Implementing Regular Expressions – series of articles by Russ Cox, author of RE2; Regular Expression Engines
A regular expression (shortened as regex or regexp), [1] sometimes referred to as rational expression, [2] [3] is a sequence of characters that specifies a match pattern in text. Usually such patterns are used by string-searching algorithms for "find" or "find and replace" operations on strings , or for input validation .
Tree patterns are used in some programming languages as a general tool to process data based on its structure, e.g. C#, [1] F#, [2] Haskell, [3] Java [4], ML, Python, [5] Ruby, [6] Rust, [7] Scala, [8] Swift [9] and the symbolic mathematics language Mathematica have special syntax for expressing tree patterns and a language construct for ...
Perl Compatible Regular Expressions (PCRE) is a library written in C, which implements a regular expression engine, inspired by the capabilities of the Perl programming language. Philip Hazel started writing PCRE in summer 1997. [ 3 ]
With online algorithms the pattern can be processed before searching but the text cannot. In other words, online techniques do searching without an index. Early algorithms for online approximate matching were suggested by Wagner and Fischer [3] and by Sellers. [2] Both algorithms are based on dynamic programming but solve different problems.
Each regular expression is associated with a production rule in the lexical grammar of the programming language that evaluates the lexemes matching the regular expression. These tools may generate source code that can be compiled and executed or construct a state transition table for a finite-state machine (which is plugged into template code ...
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.
However, it is a useful algorithm for multiple pattern search. To find any of a large number, say k, fixed length patterns in a text, a simple variant of the Rabin–Karp algorithm uses a Bloom filter or a set data structure to check whether the hash of a given string belongs to a set of hash values of patterns we are looking for: