Comprises Byte Vector related distance measures.
Euclidean distance the square root of the sum of the square of differences of each coordinate of the byte vectors.
Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them.
Minkowski distance is the generalization of the Manhattan, Euclidean and the max distances.
Manhattan distance the sum of the absolute values of differences of each coordinate of the byte vectors.
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