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

 riem.pdist R Documentation

## 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