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.
cov_mat(x1, x2 = x1, g = NULL, pow = 2, lengthscale = 1)Covariance matrix with length(x1) rows and length(x2) columns
used by the Gaussian process optimiser.
numeric vector, with length corresponding to the number of rows in the returned matrix.
numeric vector, with length corresponding to the number of columns
in the returned matrix. If not specified, x1 will be used for x2.
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.
single numeric value, the power that all distances are raised to.
Defaults to 2, corresponding to pairwise, squared, Euclidean distances.
single numerical value; lengthscale hyperparameter that
the matrix returned from pow_abs_dist() is divided by before the inverse
exponentiation is done.