Distances/Kernels

# Distances API Reference

## Functions

``(K::Kernel)(X)``

This is a convenience function to allow for one-line construction of kernels from a Kernel object `K` and new data `X`.

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``Kernel(X::Array)``

Default constructor for Kernel object. Returns the linear kernel of `X`.

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``NearestNeighbors(DistanceMatrix)``

Returns the nearest neighbor adjacency matrix from a given `DistanceMatrix`.

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``CauchyKernel(X, Y, sigma)``

Creates a Cauchy kernel from Arrays `X` and `Y` using hyperparameters `sigma`.

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``CauchyKernel(X, sigma)``

Creates a Cauchy kernel from Array `X` using hyperparameters `sigma`.

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``CenterKernelMatrix(X)``

Returns a centered kernel matrix.

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``EuclideanDistance(X, Y)``

Returns the euclidean distance matrix of X and Y such that the columns are the samples in Y.

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``EuclideanDistance(X)``

Returns the Grahm aka the euclidean distance matrix of `X`.

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``GaussianKernel(X, Y, sigma)``

Creates a Gaussian/RBF kernel from Arrays `X` and `Y` with hyperparameter `sigma`.

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``GaussianKernel(X, sigma)``

Creates a Gaussian/RBF kernel from Array `X` using hyperparameter `sigma`.

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``InClassAdjacencyMatrix(DistanceMatrix, YHOT, K = 1)``

Computes the in class Adjacency matrix with K nearest neighbors.

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``LevenshteinDistance(s::AbstractString, t::AbstractString)``

Calculates the LevenshteinDistance aka the edit distance between 2 strings.

Borrowed from: https://rosettacode.org/wiki/Levenshtein_distance#Julia

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``LinearKernel(X, Y, c)``

Creates a Linear kernel from Arrays `X` and `Y` with hyperparameter `C`.

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``LinearKernel(X, c)``

Creates a Linear kernel from Array `X` and hyperparameter `C`.

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``ManhattanDistance(X, Y)``

Returns the Manhattan distance matrix of X and Y such that the columns are the samples in Y.

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``ManhattanDistance(X)``

Returns the Manhattan distance matrix of `X`.

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``MinkowskiDistance(X, Y, p)``

Returns the Minkowski distance matrix of `X` and `Y` using order `p` such that the columns are the samples in `Y`.

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``MinkowskiDistance(X, p)``

Returns the Manhattan distance matrix of `X` using order `p`.

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``NearestNeighbors(DistanceMatrix, N)``

Returns a matrix of dimensions DistanceMatrix rows, by N columns. Basically this goes through each row and finds the ones corresponding column which has the smallest distance.

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``OutOfClassAdjacencyMatrix(DistanceMatrix, YHOT, K = 1)``

Computes the out of class Adjacency matrix with K nearest neighbors.

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``SquareEuclideanDistance(X, Y)``

Returns the squared euclidean distance matrix of X and Y such that the columns are the samples in Y.

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``SquareEuclideanDistance(X)``

Returns the squared Grahm aka the euclidean distance matrix of `X`.

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