R/data.preprocessing.R

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

dedup.backend = function(data, threshold, debug = FALSE) {

  .Call(call_dedup,
        data = data,
        threshold = threshold,
        complete = attr(data, "metadata")$complete.nodes,
        debug = debug)

}#DEDUP.BACKEND

discretize.backend = function(data, method, breaks, ordered = FALSE, extra.args,
    debug = FALSE) {

  if (method %in% c("quantile", "interval")) {

    discretized = marginal.discretize.backend(data = data, method = method,
                    breaks = breaks, ordered = ordered, debug = debug)

  }#THEN
  else if (method == "hartemink") {

    discretized = joint.discretize.backend(data = data, method = method,
                    breaks = breaks, ordered = ordered,
                    initial.discretization = extra.args$idisc,
                    initial.breaks = extra.args$ibreaks, debug = debug)

  }#ELSE

  # ensure that the attribute with the metadata set by check.data() is removed.
  attr(discretized, "metadata") = NULL

  return(discretized)

}#DISCRETIZE.BACKEND

marginal.discretize.backend = function(data, method, breaks, ordered = FALSE,
    debug = FALSE) {

  .Call(call_marginal_discretize,
        data = data,
        method = method,
        breaks = as.integer(breaks),
        ordered = ordered,
        debug = debug)

}#MARGINAL.DISCRETIZE.BACKEND

joint.discretize.backend = function(data, method, breaks, ordered = FALSE,
    initial.discretization = "quantile", initial.breaks, debug = FALSE) {

  .Call(call_joint_discretize,
        data = data,
        method = method,
        breaks = as.integer(breaks),
        ordered = ordered,
        initial.discretization = initial.discretization,
        initial.breaks = as.integer(initial.breaks),
        debug = debug)

}#JOINT.DISCRETIZE.BACKEND