KernelWhiteNoise: R6 class for White Noise Kernels

In BKTR: Bayesian Kernelized Tensor Regression

R6 class for White Noise Kernels

Description

R6 class for White Noise Kernels

Super class

BKTR::Kernel -> KernelWhiteNoise

Public fields

has_dist_matrix

Identify if the kernel has a distance matrix or not

name

The kernel's name

Methods

Public methods

• KernelWhiteNoise$new()

• KernelWhiteNoise$core_kernel_fn()

• KernelWhiteNoise$clone()

Inherited methods

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

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Method new()

Usage

KernelWhiteNoise$new(kernel_variance = 1, jitter_value = NULL)

Arguments

kernel_variance

Numeric: The variance of the kernel

jitter_value

Numeric: The jitter value to add to the kernel matrix

Returns

A new KernelWhiteNoise object.

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Method core_kernel_fn()

Method to compute the core kernel's covariance matrix

Usage

KernelWhiteNoise$core_kernel_fn()

Returns

The core kernel's covariance matrix

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Method clone()

The objects of this class are cloneable with this method.

Usage

KernelWhiteNoise$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a new white noise kernel
k_white_noise <- KernelWhiteNoise$new()
# Set the kernel's positions
positions_df <- data.frame(x=c(-4, 0, 3), y=c(-2, 0, 2))
k_white_noise$set_positions(positions_df)
# Generate the kernel's covariance matrix
k_white_noise$kernel_gen()

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