IGP_base | R Documentation |
UGP Class providing object with methods for fitting a GP model
UGP Class providing object with methods for fitting a GP model
R6Class
object.
Object of R6Class
with methods for fitting GP model.
For full documentation of each method go to https://github.com/CollinErickson/UGP/
new(X=NULL, Z=NULL, package=NULL, corr="gauss",
estimate.nugget=T, nugget0=F, ...)
This method
is used to create object of this class with X
and Z
as the data.
The package tells it which package to fit the GP model.
Xall=NULL, Zall=NULL, Xnew=NULL, Znew=NULL, ...
This method updates the model, adding new data if given, then running optimization again.
X
Design matrix
Z
Responses
N
Number of data points
D
Dimension of data
N
Number of data points
D
Dimension of data
.init()
IGP_base$.init(...)
.update()
IGP_base$.update(...)
.predict()
IGP_base$.predict(...)
.predict.se()
IGP_base$.predict.se(...)
.predict.var()
IGP_base$.predict.var(...)
.delete()
IGP_base$.delete(...)
new()
IGP_base$new( X = NULL, Z = NULL, package = NULL, corr = "gauss", estimate.nugget = TRUE, nugget0 = 1e-08, ... )
init()
IGP_base$init(X = NULL, Z = NULL, ...)
update()
IGP_base$update(Xall = NULL, Zall = NULL, Xnew = NULL, Znew = NULL, ...)
predict()
IGP_base$predict(XX, se.fit = FALSE, ...)
predict.se()
IGP_base$predict.se(XX, ...)
predict.var()
IGP_base$predict.var(XX, ...)
grad()
IGP_base$grad(XX, num = FALSE)
grad_num()
IGP_base$grad_num(XX)
grad_from_theta()
IGP_base$grad_from_theta(XX, theta)
grad_norm()
IGP_base$grad_norm(XX)
sample()
IGP_base$sample(XX, n = 1)
theta()
IGP_base$theta()
nugget()
IGP_base$nugget()
s2()
IGP_base$s2()
mean()
IGP_base$mean()
max.var()
IGP_base$max.var()
at.max.var()
IGP_base$at.max.var(X, val = 0.9)
prop.at.max.var()
IGP_base$prop.at.max.var( Xlims = matrix(c(0, 1), nrow = ncol(self$X), ncol = 2, byrow = T), n = 200, val = 0.9 )
plot()
IGP_base$plot()
delete()
IGP_base$delete(...)
finalize()
IGP_base$finalize(...)
clone()
The objects of this class are cloneable with this method.
IGP_base$clone(deep = FALSE)
deep
Whether to make a deep clone.
n <- 40 d <- 2 n2 <- 20 f1 <- function(x) {sin(2*pi*x[1]) + sin(2*pi*x[2])} X1 <- matrix(runif(n*d),n,d) Z1 <- apply(X1,1,f1) + rnorm(n, 0, 1e-3) X2 <- matrix(runif(n2*d),n2,d) Z2 <- apply(X2,1,f1) XX1 <- matrix(runif(10),5,2) ZZ1 <- apply(XX1, 1, f1) u <- IGP(package='laGP',X=X1,Z=Z1, corr="gauss") cbind(u$predict(XX1), ZZ1) u$predict.se(XX1) u$update(Xnew=X2,Znew=Z2) u$predict(XX1) u$delete()
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