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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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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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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