#' Generate Grid
#'
#' Generates a uniform grid over the distribution of the time2event variable,
#' calculates closest point and returns this point for each input time2event
#' element. Memory consumption will increase when performing the randomForest
#' model with many unique time2event values. Therefore, we offer a reduction of
#' the time2event values by choosing closest elements in a grid.
#'
#' @param t2e numeric vector with time2event values
#' @param grid_length number of grid elements
#'
#' @return a list with new_t2e and grid_error
#'
#' @export
generate_grid <- function(t2e, grid_length = 250) {
t2e_grid <- seq(0, max(t2e), length.out = grid_length)
d <- cpp_weightedDistanceXY(matrix(t2e_grid), matrix(t2e), 1)
o <- cpp_orderMatrix(d, 0, 1) |>
as.numeric()
new_grid <- t2e_grid[o]
d |>
as.data.frame() |>
as.list() |>
purrr::map2(.y = o, .f = function(x, y) {
x[y]
}) |>
purrr::reduce(c) -> grid_error
list(grid_error = grid_error,
new_t2e = new_grid)
}
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