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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 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.
Similarity between strings. For comparing strings, there are various measures of string similarity that can be used. Some of these methods include edit distance, Levenshtein distance, Hamming distance, and Jaro distance. The best-fit formula is dependent on the requirements of the application.
In information theory, the Hamming distance between two strings or vectors of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or equivalently, the minimum number of errors that could have transformed one string into the other.
(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 ...
COBOL uses the STRING statement to concatenate string variables. MATLAB and Octave use the syntax "[x y]" to concatenate x and y. Visual Basic and Visual Basic .NET can also use the "+" sign but at the risk of ambiguity if a string representing a number and a number are together. Microsoft Excel allows both "&" and the function "=CONCATENATE(X,Y)".
When taken as a string similarity measure, the coefficient may be calculated for two strings, x and y using bigrams as follows: [11] = + where n t is the number of character bigrams found in both strings, n x is the number of bigrams in string x and n y is the number of bigrams in string y. For example, to calculate the similarity between:
The higher the Jaro–Winkler distance for two strings is, the less similar the strings are. The score is normalized such that 0 means an exact match and 1 means there is no similarity. The original paper actually defined the metric in terms of similarity, so the distance is defined as the inversion of that value (distance = 1 − similarity).