Distance Measures

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.

NearestNeighbors(DistanceMatrix)

Returns the nearest neighbor adjacency matrix from a given DistanceMatrix.

CauchyKernel(X, Y, sigma)

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

CauchyKernel(X, sigma)

Creates a Cauchy kernel from Array X using hyperparameters sigma.

EuclideanDistance(X, Y)

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

EuclideanDistance(X)

Returns the Grahm aka the euclidean distance matrix of X.

GaussianKernel(X, Y, sigma)

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

GaussianKernel(X, sigma)

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

InClassAdjacencyMatrix(DistanceMatrix, YHOT, K = 1)

Computes the in class Adjacency matrix with K nearest neighbors.

LinearKernel(X, Y, c)

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

LinearKernel(X, c)

Creates a Linear kernel from Array X and hyperparameter C.

ManhattanDistance(X, Y)

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

ManhattanDistance(X)

Returns the Manhattan distance matrix of X.

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.

OutOfClassAdjacencyMatrix(DistanceMatrix, YHOT, K = 1)

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

SquareEuclideanDistance(X, Y)

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

SquareEuclideanDistance(X)

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