IGP_LOOEC_GauPro_kernel | R Documentation |
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/IGP/
new(X=NULL, Z=NULL, package=NULL,
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.
update(Xall=NULL, Zall=NULL, Xnew=NULL, Znew=NULL, ...)
This method updates the model, adding new data if given, then running optimization again.
IGP::IGP
-> IGP_LOOEC_GauPro_kernel
X
Design matrix
Z
Responses
N
Number of data points
D
Dimension of data
X
Design matrix
Z
Responses
N
Number of data points
D
Dimension of data
IGP::IGP$at.max.var()
IGP::IGP$delete()
IGP::IGP$finalize()
IGP::IGP$grad()
IGP::IGP$grad_from_theta()
IGP::IGP$grad_norm()
IGP::IGP$grad_num()
IGP::IGP$init()
IGP::IGP$initialize()
IGP::IGP$max.var()
IGP::IGP$mean()
IGP::IGP$nugget()
IGP::IGP$plot()
IGP::IGP$predict()
IGP::IGP$predict.se()
IGP::IGP$predict.var()
IGP::IGP$prop.at.max.var()
IGP::IGP$s2()
IGP::IGP$sample()
IGP::IGP$theta()
IGP::IGP$update()
.init()
IGP_LOOEC_GauPro_kernel$.init(..., kernel = NULL, theta = NULL)
.update()
IGP_LOOEC_GauPro_kernel$.update(...)
.predict()
IGP_LOOEC_GauPro_kernel$.predict(XX, se.fit, ...)
.predict.se()
IGP_LOOEC_GauPro_kernel$.predict.se(XX, ...)
.predict.var()
IGP_LOOEC_GauPro_kernel$.predict.var(XX, ...)
.grad()
IGP_LOOEC_GauPro_kernel$.grad(XX)
.delete()
IGP_LOOEC_GauPro_kernel$.delete(...)
.theta()
IGP_LOOEC_GauPro_kernel$.theta()
.nugget()
IGP_LOOEC_GauPro_kernel$.nugget()
.s2()
IGP_LOOEC_GauPro_kernel$.s2()
.mean()
IGP_LOOEC_GauPro_kernel$.mean()
clone()
The objects of this class are cloneable with this method.
IGP_LOOEC_GauPro_kernel$clone(deep = FALSE)
deep
Whether to make a deep clone.
n <- 30 d <- 2 n2 <- 10 f1 <- function(x) {sin(2*pi*x[1]) + sin(2*pi*x[2])} X1 <- matrix(runif(n*d),n,d) Z1 <- apply(X1,1,f1) + rnorm(n, 0, 1e-3) X2 <- matrix(runif(n2*d),n2,d) Z2 <- apply(X2,1,f1) XX1 <- matrix(runif(10),5,2) ZZ1 <- apply(XX1, 1, f1) u <- IGP_LOOEC_GauPro_kernel$new(X=X1,Z=Z1, parallel=FALSE) cbind(u$predict(XX1), ZZ1) u$predict.se(XX1) u$update(Xnew=X2,Znew=Z2) u$predict(XX1) u$delete() # 1D example to see difference n <- 9 d <- 1 n2 <- 20 f1 <- function(x) {x^2 * sin(2*pi*x)} X1 <- matrix(seq(0,1,l=n),n,d) Z1 <- apply(X1,1,f1) + rnorm(n, 0, 1e-1) X2 <- matrix(runif(n2*d),n2,d) Z2 <- apply(X2,1,f1) XX1 <- matrix(runif(10),5,2) ZZ1 <- apply(XX1, 1, f1) u <- IGP_LOOEC_GauPro_kernel$new(X=X1,Z=Z1, parallel=FALSE) u$plot() u$mod$tmod$plot1D() u$update(Xnew=X2,Znew=Z2) u$plot() u$mod$tmod$plot1D() u$delete()
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