k2gaussian: 2D Gaussian Kernel Matrix for '"lineqGP"' Models.
In lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints

Description Usage Arguments Value Author(s) Examples

Compute the 2D Gaussian kernel matrix for "lineqGP" models. attr: "gradient".

1	k2gaussian(x1, x2, par, d = 2)

`x1`	a matrix with the first couple of input locations.
`x2`	a matrix with the second couple of input locations.
`par`	the values of the kernel parameters (variance, lengthscales).
`d`	a number corresponding to the dimension of the input space.

Kernel matrix K(x1,x2) (or K(x1,x1) if x2 is not defined).

A. F. Lopez-Lopera.

xgrid <- seq(0, 1, 0.1)
x <- as.matrix(expand.grid(xgrid, xgrid))
K <- k2gaussian(x, x, par =  c(1, 0.1))
image(K, main = "covariance matrix using a 2D Gaussian kernel")