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  2. 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.

  3. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Chebyshev distance; 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.

  4. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    The normalized angle, referred to as angular distance, between any two vectors and is a formal distance metric and can be calculated from the cosine similarity. [5] The complement of the angular distance metric can then be used to define angular similarity function bounded between 0 and 1, inclusive.

  5. 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.

  6. 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.

  7. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  8. Small-angle approximation - Wikipedia

    en.wikipedia.org/wiki/Small-angle_approximation

    The sine and tangent small-angle approximations are used in relation to the double-slit experiment or a diffraction grating to develop simplified equations like the following, where y is the distance of a fringe from the center of maximum light intensity, m is the order of the fringe, D is the distance between the slits and projection screen ...

  9. C mathematical functions - Wikipedia

    en.wikipedia.org/wiki/C_mathematical_functions

    Most of the mathematical functions are defined in <math.h> (<cmath> header in C++). The functions that operate on integers, such as abs, labs, div, and ldiv, are instead defined in the <stdlib.h> header (<cstdlib> header in C++). Any functions that operate on angles use radians as the unit of angle. [1]