cov_mat: Estimates covariance matrices used by Gaussian process...

In adaptr: Adaptive Trial Simulator

Estimates covariance matrices used by Gaussian process optimisation

Description

Used internally, estimates covariance matrices used by the Gaussian process optimisation function. Calculates pairwise absolute distances raised to a power (which defaults to 2) using the pow_abs_dist() function, divides the result by a lengthscale hyperparameter (which defaults to 1, i.e., no changes due to division), and subsequently returns the inverse exponentiation of the resulting matrix.

Usage

cov_mat(x1, x2 = x1, g = NULL, pow = 2, lengthscale = 1)

Arguments

x1	numeric vector, with length corresponding to the number of rows in the returned matrix.
x2	numeric vector, with length corresponding to the number of columns in the returned matrix. If not specified, x1 will be used for x2.
g	single numerical value; jitter/nugget value added to the diagonal if not NULL (the default); should be supplied if x1 is the same as x2, to avoid potentially negative values in the matrix diagonal due to numerical instability.
pow	single numeric value, the power that all distances are raised to. Defaults to 2, corresponding to pairwise, squared, Euclidean distances.
lengthscale	single numerical value; lengthscale hyperparameter that the matrix returned from pow_abs_dist() is divided by before the inverse exponentiation is done.

Value

Covariance matrix with length(x1) rows and length(x2) columns used by the Gaussian process optimiser.

adaptr documentation built on May 29, 2024, 7:48 a.m.