kernel_product: Gaussian Kernel R6 class

In CollinErickson/GauPro: Gaussian Process Fitting

Gaussian Kernel R6 class

Description

Gaussian Kernel R6 class

Gaussian Kernel R6 class

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_product

Public fields

k1

kernel 1

k2

kernel 2

s2

Variance

Active bindings

k1pl

param length of kernel 1

k2pl

param length of kernel 2

s2_est

Is s2 being estimated?

Methods

Public methods

• kernel_product$new()

• kernel_product$k()

• kernel_product$param_optim_start()

• kernel_product$param_optim_start0()

• kernel_product$param_optim_lower()

• kernel_product$param_optim_upper()

• kernel_product$set_params_from_optim()

• kernel_product$dC_dparams()

• kernel_product$C_dC_dparams()

• kernel_product$dC_dx()

• kernel_product$s2_from_params()

• kernel_product$print()

• kernel_product$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()

---

Method new()

Is s2 being estimated?

Length of the parameters of k1

Length of the parameters of k2

Initialize kernel

Usage

kernel_product$new(k1, k2, useC = TRUE)

Arguments

k1

Kernel 1

k2

Kernel 2

useC

Should C code used? Not applicable for kernel product.

---

Method k()

Calculate covariance between two points

Usage

kernel_product$k(x, y = NULL, params, ...)

Arguments

x

vector.

y

vector, optional. If excluded, find correlation of x with itself.

params

parameters to use instead of beta and s2.

...

Not used

---

Method param_optim_start()

Starting point for parameters for optimization

Usage

kernel_product$param_optim_start(jitter = F, y)

Arguments

jitter

Should there be a jitter?

y

Output

---

Method param_optim_start0()

Starting point for parameters for optimization

Usage

kernel_product$param_optim_start0(jitter = F, y)

Arguments

jitter

Should there be a jitter?

y

Output

---

Method param_optim_lower()

Lower bounds of parameters for optimization

Usage

kernel_product$param_optim_lower()

---

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

kernel_product$param_optim_upper()

---

Method set_params_from_optim()

Set parameters from optimization output

Usage

kernel_product$set_params_from_optim(optim_out)

Arguments

optim_out

Output from optimization

---

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

kernel_product$dC_dparams(params = NULL, C, X, C_nonug, nug)

Arguments

params

Kernel parameters

C

Covariance with nugget

X

matrix of points in rows

C_nonug

Covariance without nugget added to diagonal

nug

Value of nugget

---

Method C_dC_dparams()

Calculate covariance matrix and its derivative with respect to parameters

Usage

kernel_product$C_dC_dparams(params = NULL, X, nug)

Arguments

params

Kernel parameters

X

matrix of points in rows

nug

Value of nugget

---

Method dC_dx()

Derivative of covariance with respect to X

Usage

kernel_product$dC_dx(XX, X)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

---

Method s2_from_params()

Get s2 from params vector

Usage

kernel_product$s2_from_params(params, s2_est = self$s2_est)

Arguments

params

parameter vector

s2_est

Is s2 being estimated?

---

Method print()

Print this object

Usage

kernel_product$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

kernel_product$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

k1 <- Exponential$new(beta=1)
k2 <- Matern32$new(beta=2)
k <- k1 * k2
k$k(matrix(c(2,1), ncol=1))

CollinErickson/GauPro documentation built on March 25, 2024, 11:20 p.m.