R/helper.R

In Mufabo/Rrobustsp: Robust Signal Processing

sweep_sparse <- function(x, margin, stats, fun = "-") {
  f <- match.fun(fun)
  if (margin == 1) {
    idx <- x@i + 1
  } else {
    idx <- x@j + 1
  }
  x@x <- f(x@x, stats[idx])
  return(x)
}

#' repmat
#'  matlab like repmat, vector v is replicate s times columnwise into a matrix
repmat <- function(v, s) matrix(v, nrow=length(v), ncol=s)

#' mat_sign
#'
#' sign that also works for complex numbers
mat_sign <- function(x){
  if(is.complex(x)){
    res <- x
    res[x != 0] <- x[x != 0] / abs(x[x != 0])
    }
  else res <- sign(x)
  return(res)

}

orth <- function(x){
  # rankMatrix is from Matrix library
  svd(x)$u[,1:rankMatrix(x)[[1]]]}

#' sqrtm
#'
#' computes square root of matrix via Jordan Normal form
#'
sqrtm <- function(mat){
  X <- eigen(mat)
  T <- X$vectors
  vals <- sqrt(as.complex(X$values))
  J <- diag(vals)
  return(T %*% J %*% solve(T))
}

#' inf_norm
#'
#' norm(mat , type = 'I') discards
#' the imaginary part of complex matrices
#'
inf_norm <- function(mat){
  return(max(rowSums(abs(mat))))
}

#' mat_stem
#'
#' matlab-like stem plot
#'
#' @references
#'
#' https://www.r-bloggers.com/matlab-style-stem-plot-with-r/
#'
#' @export
mat_stem <- function(x,y,pch=16,linecol=1,clinecol=1, ...){
  if (missing(y)){
    y = x
    x = 1:length(x) }
  graphics::plot(x,y,pch=pch,...)
  for (i in 1:length(x)){
    graphics::lines(c(x[i],x[i]), c(0,y[i]),col=linecol)
  }
  graphics::lines(c(x[1]-2,x[length(x)]+2), c(0,0),col=clinecol)
}