R/matlab_dominateset.R

In twesleyb/neten: R Implementation of Network Enhancement

#' dominateset
#'
#' R implementation of Network Enhacement
#'
#' @param affy_matrix - input weighted N x N adjacency matrix
#'
#' @param NR_OF_KNN - number of neighbors
#'
#' @return None
#'
#' @author danro9685, \href{https://github.com/danro9685/CIMLR}{github}
#' @author Bo Wang, \href{https://github.com/wangboyunze}{github}
#' @author microbma, \href{https://github.com/microbma}{github}
#' @author Tyler Bradshaw, \href{https://github.com/twesleyb}{github}
#'
#' @references \href{https://bit.ly/2RS5pLX}{Wang, Pourshafeie, Zitnik, et al., 2018}
#'
#' @keywords network enhancement
#'
#' @export
dominateset <- function(aff_matrix, NR_OF_KNN) {
  # source("Length.R")
  eps <- 2e-16

  A <- t(apply(aff_matrix, MARGIN = 1, function(x) sort(x, decreasing = TRUE)))
  B <- t(apply(aff_matrix, MARGIN = 1, function(x) order(x, decreasing = TRUE)))

  res <- A[, 1:NR_OF_KNN]
  inds <- matrix(1:Length(aff_matrix), nrow = Length(aff_matrix), ncol = NR_OF_KNN)
  loc <- B[, 1:NR_OF_KNN]
  PNN_matrix1 <- matrix(0, ncol = ncol(aff_matrix), nrow = nrow(aff_matrix))
  idx_ <- (as.vector(loc) - 1) * nrow(aff_matrix) + as.vector(inds)
  PNN_matrix1[idx_] <- as.vector(res)
  PNN_matrix <- (PNN_matrix1 + t(PNN_matrix1)) / 2
  return(PNN_matrix)
}