# R/wrap10euclidean.R In Riemann: Learning with Data on Riemannian Manifolds

#### Documented in wrap.euclidean

#' Prepare Data on Euclidean Space
#'
#' Euclidean space \eqn{\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 \eqn{p}-dimensional vectors.
#'
#' @param input data vectors to be wrapped as \code{riemdata} class. Following inputs are considered,
#' \describe{
#' \item{matrix}{an \eqn{(n \times p)} matrix of row observations.}
#' \item{list}{a length-\eqn{n} list whose elements are length-\eqn{p} vectors.}
#' }
#'
#' @return a named \code{riemdata} S3 object containing
#' \describe{
#'   \item{data}{a list of \eqn{(p\times 1)} matrices in \eqn{\mathbf{R}^p}.}
#'   \item{size}{dimension of the ambient space.}
#'   \item{name}{name of the manifold of interests, \emph{"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)
#'
#' @concept wrapper
#' @export
wrap.euclidean <- function(input){
## TAKE EITHER 2D ARRAY {n x p} OR A LIST
#  1. data format
if (is.matrix(input)){
N = nrow(input)
tmpdata = list()
for (i in 1:N){
tmpdata[[i]] = as.vector(input[i,])
}
} else if (is.list(input)){
tmpdata = input
} else {
stop("* wrap.euclidean : input should be either a 2d matrix or a list.")
}
#  2. check all same size
if (!check_list_eqsize(tmpdata, check.square=FALSE)){
stop("* wrap.euclidean : elements are not vectors of same size.")
}
#  3. check each element
N = length(tmpdata)
for (n in 1:N){
tmpdata[[n]] = matrix(tmpdata[[n]], ncol=1)
}

## WRAP AND RETURN THE S3 CLASS
output = list()
output$data = tmpdata output$size = dim(tmpdata[[1]])
output\$name = "euclidean"
return(structure(output, class="riemdata"))
}


## Try the Riemann package in your browser

Any scripts or data that you put into this service are public.

Riemann documentation built on June 20, 2021, 5:07 p.m.