pfocal (version 1.0.0)

euclidean_distance_kernel: Compute an Distance kernel

Description

Functions to compute a distance kernel.

Usage

euclidean_distance_kernel(vertical_radius, horizontal_radius = vertical_radius)
manhattan_distance_kernel(vertical_radius, horizontal_radius = vertical_radius)
minkowski_distance_kernel(
  p,
  vertical_radius,
  horizontal_radius = vertical_radius
)
chebyshev_distance_kernel(vertical_radius, horizontal_radius = vertical_radius)
vertical_distance_kernel(vertical_radius, horizontal_radius = vertical_radius)
horizontal_distance_kernel(
  vertical_radius,
  horizontal_radius = vertical_radius
)
distance_kernel(vertical_radius, horizontal_radius = vertical_radius)

Arguments

vertical_radius

[numeric] The kernel's radius in the vertical dimension.

horizontal_radius

[numeric] The kernel's radius in the horizontal dimension.

p

[numeric] Exponent parameter for the Minkowski distance.

Value

A matrix corresponding to the kernel.

Examples

# NOT RUN {
distance_kernel(vertical_radius = 2, horizontal_radius = 2)
euclidean_distance_kernel(vertical_radius = 2, horizontal_radius = 2)
manhattan_distance_kernel(vertical_radius = 2, horizontal_radius = 2)
minkowski_distance_kernel(vertical_radius = 2, horizontal_radius = 2, p = 1)
chebyshev_distance_kernel(vertical_radius = 2, horizontal_radius = 2)
vertical_distance_kernel(vertical_radius = 2, horizontal_radius = 2)
horizontal_distance_kernel(vertical_radius = 2, horizontal_radius = 2)

# }