IGP_GauPro_kernel: IGP R6 object for fitting GauPro model

In IGP: Interchangeable Gaussian Process Models

IGP R6 object for fitting GauPro model

Description

Class providing object with methods for fitting a GP model

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Methods

Documentation

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.

Super class

IGP::IGP -> IGP_GauPro_kernel

Public fields

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

Active bindings

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

Methods

Public methods

• IGP_GauPro_kernel$.init()

• IGP_GauPro_kernel$.update()

• IGP_GauPro_kernel$.predict()

• IGP_GauPro_kernel$.predict.se()

• IGP_GauPro_kernel$.predict.var()

• IGP_GauPro_kernel$.grad()

• IGP_GauPro_kernel$.delete()

• IGP_GauPro_kernel$.theta()

• IGP_GauPro_kernel$.nugget()

• IGP_GauPro_kernel$.s2()

• IGP_GauPro_kernel$.mean()

• IGP_GauPro_kernel$clone()

Inherited methods

• 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()

---

Method .init()

Usage

IGP_GauPro_kernel$.init(..., kernel = NULL, theta = NULL)

---

Method .update()

Usage

IGP_GauPro_kernel$.update(...)

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Method .predict()

Usage

IGP_GauPro_kernel$.predict(XX, se.fit, ...)

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Method .predict.se()

Usage

IGP_GauPro_kernel$.predict.se(XX, ...)

---

Method .predict.var()

Usage

IGP_GauPro_kernel$.predict.var(XX, ...)

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Method .grad()

Usage

IGP_GauPro_kernel$.grad(XX)

---

Method .delete()

Usage

IGP_GauPro_kernel$.delete(...)

---

Method .theta()

Usage

IGP_GauPro_kernel$.theta()

---

Method .nugget()

Usage

IGP_GauPro_kernel$.nugget()

---

Method .s2()

Usage

IGP_GauPro_kernel$.s2()

---

Method .mean()

Usage

IGP_GauPro_kernel$.mean()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

IGP_GauPro_kernel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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_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()

IGP documentation built on Jan. 16, 2023, 1:07 a.m.