R/Prochloro_Random_Neighbour2.R

In phytoclass: Estimate Chla Concentrations of Phytoplankton Groups

#' Selects a random neighbour for the simulated annealing algorithm.
#' 
#' @keywords internal
#'
#' @param Fn xx
#' @param Temp xx
#' @param chlv xx
#' @param s_c xx
#' @param place  xx
#' @param S  xx
#' @param cm  xx
#' @param min.val  xx
#' @param max.val  xx
#' @param chlvp  xx

#'
#' @return
#' 
#' @examples
#' @importFrom stats runif
Prochloro_Random_Neighbour_2 <- function(Fn, Temp, chlv, s_c, place, S, cm, min.val, max.val,chlvp) 
  {
  s_c <- vectorise(s_c[, 1:ncol(s_c) - 1])
  s_c <- s_c[1:length(s_c)-1]
  SE <- Prochloro_Wrangling(Fn, min.val, max.val)[[3]]
  SE <- SE[1:length(SE)-1]
  minF <- Prochloro_Wrangling(Fn, min.val, max.val)[[1]]
  minF <- minF[1:length(minF)-1]
  maxF <- Prochloro_Wrangling(Fn, min.val, max.val)[[2]]
  maxF <- maxF[1:length(maxF)-1]
  ki <- maxF - minF
  rand <- round(runif(n = length(s_c), -1, 1), 4)
  SA <- (SE + (Temp) * ki * rand)
  SA <- as.vector(unlist(SA))
  
  d <- which(SA < minF | SA > maxF)
  loop <- 1
  max_loops <- 100 # Adjust this if needed
  
  while (length(d) > 0) {
    loop <- loop + 1
    nr <- round(runif(length(d), -1, 1), 4)
    minr <- as.vector(unlist(minF))
    maxr <- as.vector(unlist(maxF))
    mind <- minr[d]
    maxd <- maxr[d]
    kir <- maxd - mind
    SA2 <- (SE[d] + (Temp) * kir * nr)
    SA[d] <- SA2
    d <- which(SA < minF | SA > maxF)
    
    # Exit condition after max loops with a safe fallback
    if (loop > max_loops) {
      nn <- (minF[d]+maxF[d])/2
      f <- round(runif(n=length(d),(minF[d]*1.20),(maxF[d]*0.80)),4)
      SA[d] <- f
      d <- which(SA < minF | SA > maxF)
    }
  }
  
  
  Fn <- Fn[, 1:ncol(Fn) - 1]
  Fn <- Fn[, 1:ncol(Fn) - 1]
  Fn[Fn > 0] <- SA
  Fn <- cbind(Fn, chlvp)
  Fn <- cbind(Fn, chlv)
  colnames(Fn) <- colnames(S)
  F.n <- NNLS_MF(Fn, S, cm)
  return(F.n)
}