KernelAddComposed: R6 class for Kernels Composed via Addition

In BKTR: Bayesian Kernelized Tensor Regression

R6 class for Kernels Composed via Addition

Description

R6 class automatically generated when adding two kernels together.

Super classes

BKTR::Kernel -> BKTR::KernelComposed -> KernelAddComposed

Methods

Public methods

• KernelAddComposed$new()

• KernelAddComposed$clone()

Inherited methods

• BKTR::Kernel$add_jitter_to_kernel()
• BKTR::Kernel$kernel_gen()
• BKTR::Kernel$plot()
• BKTR::KernelComposed$core_kernel_fn()
• BKTR::KernelComposed$set_positions()

---

Method new()

Create a new KernelAddComposed object.

Usage

KernelAddComposed$new(left_kernel, right_kernel, new_name)

Arguments

left_kernel

Kernel: The left kernel to use for composition

right_kernel

Kernel: The right kernel to use for composition

new_name

String: The name of the composed kernel

Returns

A new KernelAddComposed object.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KernelAddComposed$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a new additive kernel
k_rq_plus_per <- KernelAddComposed$new(
  left_kernel = KernelRQ$new(),
  right_kernel = KernelPeriodic$new(),
  new_name = 'SE + Periodic Kernel'
)
# Set the kernel's positions
positions_df <- data.frame(x=c(-4, 0, 3), y=c(-2, 0, 2))
k_rq_plus_per$set_positions(positions_df)
# Generate the kernel's covariance matrix
k_rq_plus_per$kernel_gen()

BKTR documentation built on Sept. 12, 2024, 7:04 a.m.