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In mathematics and computer science, a string metric (also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings for approximate string matching or comparison and in fuzzy string searching.
In some of these languages, this syntax is a here document or "heredoc": A token representing the string is put in the middle of a line of code, but the code continues after the starting token and the string's content doesn't appear until the next line. In other languages, the string's content starts immediately after the starting token and the ...
(compare string 1 string 2) Clojure (string= string 1 string 2) Common Lisp (string-compare string 1 string 2 p< p= p>) Scheme (SRFI 13) (string= string 1 string 2) ISLISP: compare string 1 string 2: OCaml: String.compare (string 1, string 2) Standard ML [5] compare string 1 string 2: Haskell [6] [string]::Compare(string 1, string 2) Windows ...
Any similarity between the two documents above the specified minimum will be reported (if detecting moves is selected). This is the main difference between Diff-Text and most other text comparison algorithms. Diff-Text will always match up significant similarities even if contained within non-identical or moved lines.
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.
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]
Presented here are two algorithms: the first, [8] simpler one, computes what is known as the optimal string alignment distance or restricted edit distance, [7] while the second one [9] computes the Damerau–Levenshtein distance with adjacent transpositions. Adding transpositions adds significant complexity.
For a fixed length n, the Hamming distance is a metric on the set of the words of length n (also known as a Hamming space), as it fulfills the conditions of non-negativity, symmetry, the Hamming distance of two words is 0 if and only if the two words are identical, and it satisfies the triangle inequality as well: [2] Indeed, if we fix three words a, b and c, then whenever there is a ...