Exponential: Exponential Kernel R6 class

In CollinErickson/GauPro: Gaussian Process Fitting

Exponential Kernel R6 class

Description

Exponential Kernel R6 class

Exponential Kernel R6 class

Usage

k_Exponential(
  beta,
  s2 = 1,
  D,
  beta_lower = -8,
  beta_upper = 6,
  beta_est = TRUE,
  s2_lower = 1e-08,
  s2_upper = 1e+08,
  s2_est = TRUE,
  useC = TRUE
)

Arguments

beta	Initial beta value
s2	Initial variance
D	Number of input dimensions of data
beta_lower	Lower bound for beta
beta_upper	Upper bound for beta
beta_est	Should beta be estimated?
s2_lower	Lower bound for s2
s2_upper	Upper bound for s2
s2_est	Should s2 be estimated?
useC	Should C code used? Much faster.

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Super classes

GauPro::GauPro_kernel -> GauPro::GauPro_kernel_beta -> GauPro_kernel_Exponential

Methods

Public methods

• Exponential$k()

• Exponential$kone()

• Exponential$dC_dparams()

• Exponential$dC_dx()

• Exponential$print()

• Exponential$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()
• GauPro::GauPro_kernel_beta$C_dC_dparams()
• GauPro::GauPro_kernel_beta$initialize()
• GauPro::GauPro_kernel_beta$param_optim_lower()
• GauPro::GauPro_kernel_beta$param_optim_start()
• GauPro::GauPro_kernel_beta$param_optim_start0()
• GauPro::GauPro_kernel_beta$param_optim_upper()
• GauPro::GauPro_kernel_beta$s2_from_params()
• GauPro::GauPro_kernel_beta$set_params_from_optim()

---

Method k()

Calculate covariance between two points

Usage

Exponential$k(x, y = NULL, beta = self$beta, s2 = self$s2, params = NULL)

Arguments

x

vector.

y

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

beta

Correlation parameters.

s2

Variance parameter.

params

parameters to use instead of beta and s2.

---

Method kone()

Find covariance of two points

Usage

Exponential$kone(x, y, beta, theta, s2)

Arguments

x

vector

y

vector

beta

correlation parameters on log scale

theta

correlation parameters on regular scale

s2

Variance parameter

---

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

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

Arguments

params

Kernel parameters

X

matrix of points in rows

C_nonug

Covariance without nugget added to diagonal

C

Covariance with nugget

nug

Value of nugget

---

Method dC_dx()

Derivative of covariance with respect to X

Usage

Exponential$dC_dx(XX, X, theta, beta = self$beta, s2 = self$s2)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

theta

Correlation parameters

beta

log of theta

s2

Variance parameter

---

Method print()

Print this object

Usage

Exponential$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Exponential$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

k1 <- Exponential$new(beta=0)

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