View source: R/CGGP_create_fs.R
CGGPcreate | R Documentation |
Create sparse grid GP
CGGPcreate(
d,
batchsize,
corr = "PowerExponential",
grid_sizes = c(1, 2, 4, 4, 8, 12, 20, 28, 32),
Xs = NULL,
Ys = NULL,
HandlingSuppData = "Correct",
supp_args = list()
)
d |
Input dimension |
batchsize |
Number added to design each batch for now only on predictions |
corr |
Name of correlation function to use. Must be one of "CauchySQT", "CauchySQ", "Cauchy", "Gaussian", "PowerExp", "Matern32", "Matern52". |
grid_sizes |
Size of grid refinements. |
Xs |
Supplemental X data |
Ys |
Supplemental Y data |
HandlingSuppData |
How should supplementary data be handled? * Correct: full likelihood with grid and supplemental data * Only: only use supplemental data * Ignore: ignore supplemental data |
supp_args |
Arguments used to fit if Xs and Ys are given |
CGGP
Other CGGP core functions:
CGGPappend()
,
CGGPfit()
,
predict.CGGP()
CGGPcreate(d=8,200)
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