# riem.pdist: Compute Pairwise Distances for Data In Riemann: Learning with Data on Riemannian Manifolds

## Description

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

## Usage

 1 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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 #------------------------------------------------------------------- # 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 June 20, 2021, 5:07 p.m.