kernCompute: Compute the kernel given the parameters and X.

In ahonkela/tigre: Transcription factor Inference through Gaussian process Reconstruction of Expression

Description

Compute the kernel given the parameters and X.

Usage

  kernCompute(kern, x, x2)
  kernDiagCompute(kern, x)

Arguments

kern	kernel structure to be computed.
x	first or only input data matrix (rows are data points) to the kernel computation.
x2	(optional) second input matrix to the kernel computation (forms the columns of the kernel).

Details

K <- kernCompute(kern, X) computes a kernel matrix for the given kernel type given an input data matrix.

K <- kernCompute(kern, X1, X2) computes a kernel matrix for the given kernel type given two input data matrices, one for the rows and one for the columns.

K <- kernDiagCompute(kern, X) computes the diagonal of a kernel matrix for the given kernel.

K <- *X*kernCompute(kern1, kern2, X) K <- *X*kernCompute(kern1, kern2, X1, X2) same as above, but for cross combinations of two kernels, kern1 and kern2.

Value

K	computed elements of the kernel structure.
Kd	vector containing computed diagonal elements of the kernel structure.

See Also

kernCreate

Examples

kern <- kernCreate(1, 'rbf')
K <- kernCompute(kern, as.matrix(3:8))

ahonkela/tigre documentation built on Aug. 6, 2021, 12:08 p.m.