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  2. String interpolation - Wikipedia

    en.wikipedia.org/wiki/String_interpolation

    Nim provides string interpolation via the strutils module. Formatted string literals inspired by Python F-string are provided via the strformat module, the strformat macro verifies that the format string is well-formed and well-typed, and then are expanded into Nim source code at compile-time.

  3. String metric - Wikipedia

    en.wikipedia.org/wiki/String_metric

    The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.

  4. Damerau–Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Damerau–Levenshtein_distance

    The Damerau–Levenshtein distance LD(CA, ABC) = 2 because CA → AC → ABC, but the optimal string alignment distance OSA(CA, ABC) = 3 because if the operation CA → AC is used, it is not possible to use AC → ABC because that would require the substring to be edited more than once, which is not allowed in OSA, and therefore the shortest ...

  5. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    It is at most the length of the longer string. It is zero if and only if the strings are equal. If the strings have the same size, the Hamming distance is an upper bound on the Levenshtein distance. The Hamming distance is the number of positions at which the corresponding symbols in the two strings are different.

  6. Edit distance - Wikipedia

    en.wikipedia.org/wiki/Edit_distance

    More formally, for any language L and string x over an alphabet Σ, the language edit distance d(L, x) is given by [14] (,) = (,), where (,) is the string edit distance. When the language L is context free , there is a cubic time dynamic programming algorithm proposed by Aho and Peterson in 1972 which computes the language edit distance. [ 15 ]

  7. Hamming distance - Wikipedia

    en.wikipedia.org/wiki/Hamming_distance

    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.

  8. Arithmetic coding - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_coding

    For example, the number 457 is actually 4×10 2 + 5×10 1 + 7×10 0, where base 10 is presumed but not shown explicitly. Initially, we will convert DABDDB into a base-6 numeral, because 6 is the length of the string. The string is first mapped into the digit string 301331, which then maps to an integer by the polynomial:

  9. Jaro–Winkler distance - Wikipedia

    en.wikipedia.org/wiki/Jaro–Winkler_distance

    In computer science and statistics, the Jaro–Winkler similarity is a string metric measuring an edit distance between two sequences. It is a variant of the Jaro distance metric [1] (1989, Matthew A. Jaro) proposed in 1990 by William E. Winkler.