basisCompute.lineqGP: Hat Basis Functions for '"lineqGP"' Models

In lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints

Description

Evaluate the hat basis functions for "lineqGP" models.

Usage

basisCompute.lineqGP(x, u, d = 1)

Arguments

x	a vector (or matrix) with the input data.
u	a vector (or matrix) with the locations of the knots.
d	a number corresponding to the dimension of the input space.

Value

A matrix with the hat basis functions. The basis functions are indexed by rows.

Comments

This function was tested mainly for 1D or 2D input spaces. It could change in future versions for higher dimensions.

Author(s)

A. F. Lopez-Lopera.

References

Lopez-Lopera, A. F., Bachoc, F., Durrande, N., and Roustant, O. (2017), "Finite-dimensional Gaussian approximation with linear inequality constraints". ArXiv e-prints [link]

Maatouk, H. and Bay, X. (2017), "Gaussian process emulators for computer experiments with inequality constraints". Mathematical Geosciences, 49(5): 557-582. [link]

Examples

x <- seq(0, 1, 1e-3)
m <- 5
u <- seq(0, 1, 1/(m-1))
Phi <- basisCompute.lineqGP(x, u, d = 1)
matplot(Phi, type = "l", lty = 2, main = "Hat basis functions with m = 5")

lineqGPR documentation built on Jan. 11, 2020, 9:23 a.m.