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Implements template {{Str find word}}.. This module looks for a word being present in a comma-separated list of words. It then returns a True or False value. By default, the True-value returned is the found word itself; the False-value is a blank string.
This template looks for a word in a comma-separated list of words. It returns a True (found) or False (not found) value. By default, the True-value returned is the found word itself; the False-value is a blank string. {{Str find word |source=alpha, foo, bar |word=foo}} (True) → foo {{Str find word |source=alpha, foo, bar |word=nov}} (False) →
For function that manipulate strings, modern object-oriented languages, like C# and Java have immutable strings and return a copy (in newly allocated dynamic memory), while others, like C manipulate the original string unless the programmer copies data to a new string.
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.
Python supports a wide variety of string operations. Strings in Python are immutable, so a string operation such as a substitution of characters, that in other programming languages might alter the string in place, returns a new string in Python. Performance considerations sometimes push for using special techniques in programs that modify ...
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 .)
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
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: