# wrap.euclidean: Prepare Data on Euclidean Space In Riemann: Learning with Data on Riemannian Manifolds

## 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

 1 wrap.euclidean(input) 

## Arguments

 input data vectors to be wrapped as riemdata class. Following inputs are considered, matrixan (n \times p) matrix of row observations. lista 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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 #------------------------------------------------------------------- # 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 June 20, 2021, 5:07 p.m.