ourMELONS/R/learn_simple_partition.R

#' @title Learn simple partition
#' @param ordered_points ordered_points
#' @param fii fii
#' @description Goes through all the ways to divide the points into two or
#' three groups. Chooses the partition which obtains highest logml.
learn_simple_partition <- function(ordered_points, fii) {
  npoints <- length(ordered_points)

  # One cluster:
  val <- calculatePopLogml(ordered_points, fii)
  bestValue <- val
  best_type <- "single"

  # Two clusters:
  for (i in 1:(npoints - 1)) {
    # The right endpoint of the first cluster.
    val_1 <- calculatePopLogml(ordered_points[1:i], fii)
    val_2 <- calculatePopLogml(ordered_points[(i + 1):length(ordered_points)], fii)
    total <- val_1 + val_2
    if (total > bestValue) {
      bestValue <- total
      best_type <- "double"
      best_i <- i
    }
  }

  # Three clusters:
  for (i in 1:(npoints - 2)) {
    for (j in (i + 1):(npoints - 1)) {
      val_1 <- calculatePopLogml(ordered_points[1:i], fii)
      val_2 <- calculatePopLogml(ordered_points[(i + 1):j], fii)
      val_3 <- calculatePopLogml(ordered_points[(j + 1):length(ordered_points)], fii)
      total <- val_1 + val_2 + val_3
      if (total > bestValue) {
        bestValue <- total
        best_type <- "triple"
        best_i <- i
        best_j <- j
      }
    }
  }

  part <- matrix(0, npoints, 1)

  switch(best_type,
    "single" = {
      part <- matrix(1, npoints, 1)
    },
    "double" = {
      part[1:best_i] <- 1
      part[(best_i + 1):length(part)] <- 2
    },
    "triple" = {
      part[1:best_i] <- 1
      part[(best_i + 1):best_j] <- 2
      part[(best_j + 1):length(part)] <- 3
    }
  )
  return(part)
}