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Predict from an MLE-Tuned GP Surrogate

Source: R/surrogate.R
predict_gp_surrogate_tuned.Rd

Companion predictor for train_gp_surrogate_tuned(). It reapplies the per-axis pre-scaling the tuner stored (so an anisotropic surrogate predicts on the same geometry it was fitted on) and adds the centred response mean back, returning predictions on the original response scale.

Usage

predict_gp_surrogate_tuned(gp, X_new)

Arguments

gp

A surrogate from train_gp_surrogate_tuned().

X_new

Numeric matrix of inputs to predict at.

Value

The list predict_gp_surrogate() returns (mean, variance, uncertainty), with mean on the original response scale.

See also

train_gp_surrogate_tuned().

Examples

set.seed(1L)
X <- matrix(runif(40L * 2L), 40L, 2L)
y <- sin(3 * X[, 1]) + 0.5 * X[, 2]^2
gp <- train_gp_surrogate_tuned(X, matrix(y, ncol = 1L))
pred <- predict_gp_surrogate_tuned(gp, X)