WLpdist: Pairwise Wasserstein-like Distance between two vMF...

In maotai: Tools for Matrix Algebra, Optimization and Inference

Pairwise Wasserstein-like Distance between two vMF distributions

Description

Given a collection of von Misees-Fisher (vMF) distributions, compute the pairwise distance using the Wasserstein-like distance from an approximate Wasserstein geometry.

Usage

WLpdist(means, concentrations)

Arguments

means	An (n \times p) matrix where each row represents the mean direction of one of the n vMF distributions.
concentrations	A length-n vector of nonnegative concentration parameters.

Value

An (n \times n) matrix of pairwise distances.

Examples

# Set seed for reproducibility
set.seed(123)

# Generate two classes of mean directions around north and south poles
means1 = array(0,c(50,2)); means1[,2] = rnorm(50, mean=1, sd=0.25)
means2 = array(0,c(50,2)); means2[,2] = rnorm(50, mean=-1, sd=0.25)
means1 = means1/sqrt(rowSums(means1^2))
means2 = means2/sqrt(rowSums(means2^2))

# Concatenate the mean directions
data_means = rbind(means1, means2)

# Generate concentration parameters
data_concentrations = rnorm(100, mean=20, sd=1)

# Compute the pairwise distance matrix
pdmat = WLpdist(data_means, data_concentrations)

# Visualise the pairwise distance matrix
opar <- par(no.readonly=TRUE)
image(pdmat, main="Pairwise Wasserstein-like Distance")
par(opar)

maotai documentation built on April 12, 2025, 2:10 a.m.