R/overlap_fixed_proposal.R

In cia: Learn and Apply Directed Acyclic Graphs for Causal Inference

#' New proposal constructor.
#' 
#' @param p Probability for each proposal in the order (split_join, node_move, 
#' swap_node, stay_still).
#' @param verbose Boolean flag to record proposal used.
#' 
#' @noRd
OverlapFixProposal <- function(p = c(0.33, 0.33, 0.0), verbose = TRUE) {
  
  stopifnot(sum(p) == 1)
  
  # function(partitioned_nodes) {
  #   
  #   alpha <- stats::runif(1)
  #   if (alpha <  sum(p[1])) {
  #     if (verbose)
  #       proposal_info <- list(proposal_used = 'node_move')
  #     
  #     current_nbd <- CalculateNodeMoveNeighbourhood(partitioned_nodes)
  #     proposed <- ProposeNodeMove(partitioned_nodes)
  #     partitioned_nodes <- proposed$partitioned_nodes
  #     rescore_nodes <- proposed$rescore_nodes
  #     new_nbd <- CalculateNodeMoveNeighbourhood(partitioned_nodes)
  #   } else (alpha < sum(p[1:2])) {
  #     if (verbose)
  #       proposal_info <- list(proposal_used = 'swap_node')
  #     
  #     current_nbd <- CalculateSwapNodeNeighbourhood(partitioned_nodes)
  #     proposed <- ProposeSwapNode(partitioned_nodes)
  #     partitioned_nodes <- proposed$partitioned_nodes
  #     rescore_nodes <- proposed$rescore_nodes
  #     new_nbd <- CalculateSwapNodeNeighbourhood(partitioned_nodes)
  #   }
  #   
  #   return(list(proposal_info = proposal_info,
  #               state = partitioned_nodes,
  #               current_nbd = current_nbd,
  #               new_nbd = new_nbd,
  #               rescore_nodes = rescore_nodes))
  # }
}