riem.pdist: Compute Pairwise Distances for Data

In Riemann: Learning with Data on Riemannian Manifolds

Compute Pairwise Distances for Data

Description

Given N observations X_1, X_2, …, X_N \in \mathcal{M}, compute pairwise distances.

Usage

riem.pdist(riemobj, geometry = c("intrinsic", "extrinsic"), as.dist = FALSE)

Arguments

riemobj	a S3 "riemdata" class for N manifold-valued data.
geometry	(case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") in geometry
as.dist	logical; if TRUE, it returns dist object, else it returns a symmetric matrix.

Value

a S3 dist object or (N\times N) symmetric matrix of pairwise distances according to as.dist parameter.

Examples

#-------------------------------------------------------------------
#          Example on Sphere : a dataset with two types
#
#  group1 : perturbed data points near (0,0,1) on S^2 in R^3
#  group2 : perturbed data points near (1,0,0) on S^2 in R^3
#-------------------------------------------------------------------
## GENERATE DATA
mydata = list()
sdval  = 0.1
for (i in 1:10){
  tgt = c(stats::rnorm(2, sd=sdval), 1)
  mydata[[i]] = tgt/sqrt(sum(tgt^2))
}
for (i in 11:20){
  tgt = c(1, stats::rnorm(2, sd=sdval))
  mydata[[i]] = tgt/sqrt(sum(tgt^2))
}
myriem = wrap.sphere(mydata)

## COMPARE TWO DISTANCES
dint = riem.pdist(myriem, geometry="intrinsic", as.dist=FALSE)
dext = riem.pdist(myriem, geometry="extrinsic", as.dist=FALSE)

## VISUALIZE
opar = par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(dint[,nrow(dint):1], main="intrinsic", axes=FALSE)
image(dext[,nrow(dext):1], main="extrinsic", axes=FALSE)
par(opar)

Riemann documentation built on March 18, 2022, 7:55 p.m.