IGP_base: UGP Class providing object with methods for fitting a GP...

In IGP: Interchangeable Gaussian Process Models

UGP Class providing object with methods for fitting a GP model

Description

UGP Class providing object with methods for fitting a GP model

UGP 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/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.

Public fields

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

Active bindings

N

Number of data points

D

Dimension of data

Methods

Public methods

• IGP_base$.init()

• IGP_base$.update()

• IGP_base$.predict()

• IGP_base$.predict.se()

• IGP_base$.predict.var()

• IGP_base$.delete()

• IGP_base$new()

• IGP_base$init()

• IGP_base$update()

• IGP_base$predict()

• IGP_base$predict.se()

• IGP_base$predict.var()

• IGP_base$grad()

• IGP_base$grad_num()

• IGP_base$grad_from_theta()

• IGP_base$grad_norm()

• IGP_base$sample()

• IGP_base$theta()

• IGP_base$nugget()

• IGP_base$s2()

• IGP_base$mean()

• IGP_base$max.var()

• IGP_base$at.max.var()

• IGP_base$prop.at.max.var()

• IGP_base$plot()

• IGP_base$delete()

• IGP_base$finalize()

• IGP_base$clone()

---

Method .init()

Usage

IGP_base$.init(...)

---

Method .update()

Usage

IGP_base$.update(...)

---

Method .predict()

Usage

IGP_base$.predict(...)

---

Method .predict.se()

Usage

IGP_base$.predict.se(...)

---

Method .predict.var()

Usage

IGP_base$.predict.var(...)

---

Method .delete()

Usage

IGP_base$.delete(...)

---

Method new()

Usage

IGP_base$new(
  X = NULL,
  Z = NULL,
  package = NULL,
  corr = "gauss",
  estimate.nugget = TRUE,
  nugget0 = 1e-08,
  ...
)

---

Method init()

Usage

IGP_base$init(X = NULL, Z = NULL, ...)

---

Method update()

Usage

IGP_base$update(Xall = NULL, Zall = NULL, Xnew = NULL, Znew = NULL, ...)

---

Method predict()

Usage

IGP_base$predict(XX, se.fit = FALSE, ...)

---

Method predict.se()

Usage

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

---

Method predict.var()

Usage

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

---

Method grad()

Usage

IGP_base$grad(XX, num = FALSE)

---

Method grad_num()

Usage

IGP_base$grad_num(XX)

---

Method grad_from_theta()

Usage

IGP_base$grad_from_theta(XX, theta)

---

Method grad_norm()

Usage

IGP_base$grad_norm(XX)

---

Method sample()

Usage

IGP_base$sample(XX, n = 1)

---

Method theta()

Usage

IGP_base$theta()

---

Method nugget()

Usage

IGP_base$nugget()

---

Method s2()

Usage

IGP_base$s2()

---

Method mean()

Usage

IGP_base$mean()

---

Method max.var()

Usage

IGP_base$max.var()

---

Method at.max.var()

Usage

IGP_base$at.max.var(X, val = 0.9)

---

Method prop.at.max.var()

Usage

IGP_base$prop.at.max.var(
  Xlims = matrix(c(0, 1), nrow = ncol(self$X), ncol = 2, byrow = T),
  n = 200,
  val = 0.9
)

---

Method plot()

Usage

IGP_base$plot()

---

Method delete()

Usage

IGP_base$delete(...)

---

Method finalize()

Usage

IGP_base$finalize(...)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

IGP_base$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(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()

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