wrap.euclidean: Prepare Data on Euclidean Space

In Riemann: Learning with Data on Riemannian Manifolds

Prepare Data on Euclidean Space

Description

Euclidean space \mathbf{R}^p is the most common space for data analysis, which can be considered as a Riemannian manifold with flat metric. Since the space of matrices is isomorphic to Euclidean space after vectorization, we consider the inputs as p-dimensional vectors.

Usage

wrap.euclidean(input)

Arguments

input

data vectors to be wrapped as riemdata class. Following inputs are considered, matrix an (n \times p) matrix of row observations. list a length-n list whose elements are length-p vectors.

Value

a named riemdata S3 object containing

data

a list of (p\times 1) matrices in \mathbf{R}^p.

size

dimension of the ambient space.

name

name of the manifold of interests, "euclidean"

Examples

#-------------------------------------------------------------------
#                 Checker for Two Types of Inputs
#
#  Generate 5 observations in R^3 in Matrix and List.
#-------------------------------------------------------------------
## DATA GENERATION
d1 = array(0,c(5,3))
d2 = list()
for (i in 1:5){
  single  = stats::rnorm(3)
  d1[i,]  = single
  d2[[i]] = single
}

## RUN
test1 = wrap.euclidean(d1)
test2 = wrap.euclidean(d2)

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