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  2. Edit distance - Wikipedia

    en.wikipedia.org/wiki/Edit_distance

    Substitution of a single symbol x for a symbol y ≠ x changes u x v to u y v (x → y). In Levenshtein's original definition, each of these operations has unit cost (except that substitution of a character by itself has zero cost), so the Levenshtein distance is equal to the minimum number of operations required to transform a to b.

  3. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    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.

  4. 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.

  5. Comparison of programming languages (string functions)

    en.wikipedia.org/wiki/Comparison_of_programming...

    In functional and list-based languages a string is represented as a list (of character codes), therefore all list-manipulation procedures could be considered string functions. However such languages may implement a subset of explicit string-specific functions as well.

  6. Approximate string matching - Wikipedia

    en.wikipedia.org/wiki/Approximate_string_matching

    This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot; substitution: coat → cost; These three operations may be generalized as forms of substitution by adding a NULL character (here symbolized by *) wherever a character has been ...

  7. Wagner–Fischer algorithm - Wikipedia

    en.wikipedia.org/wiki/Wagner–Fischer_algorithm

    The Wagner–Fischer algorithm computes edit distance based on the observation that if we reserve a matrix to hold the edit distances between all prefixes of the first string and all prefixes of the second, then we can compute the values in the matrix by flood filling the matrix, and thus find the distance between the two full strings as the last value computed.

  8. College Football Playoff bracket: Full schedule, how to watch ...

    www.aol.com/college-football-playoff-bracket...

    It is, compared with previous years, almost quadruple the number of playoff games as the past decade when there was just a four-team playoff. So buckle up – there's plenty of intrigue about how ...

  9. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    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 ...