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#' Learn
#'
#' Create or update a Probabilist neural network.
#'
#' The function \code{learn} aims to create a new Probabilist neural network with a training set, or update the training set of an already trained Probabilist neural network. It sets the parameters \code{model}, \code{set}, \code{category.column}, \code{categories}, \code{k} and \code{n} of the neural network.
#'
#' @param set Data frame representing the training set. The first column is used to define the category of each observation (set \code{category.column} if it is not the case).
#' @param nn A Probabilistic neural network with or without training.
#' @param category.column The field number of the category (1 by default).
#'
#' @seealso \code{\link{pnn-package}}, \code{\link{smooth}}, \code{\link{perf}}, \code{\link{guess}}, \code{\link{norms}}
#'
#' @export
#'
#' @return A trained Probabilist neural network.
#'
#' @examples
#' library(pnn)
#' data(norms)
#' pnn <- learn(norms)
#' pnn$model
#' pnn$set[1:10,]
#' pnn$category.column
#' pnn$categories
#' pnn$k
#' pnn$n
learn <- function(set, nn, category.column=1) {
if(missing(set)) stop("Set is missing!")
if(missing(nn)) nn <- create.pnn()
if(is.null(nn$set)) {
nn$category.column <- category.column
nn$set <- set
} else {
nn$set <- rbind(nn$set, set)
}
nn$set[,nn$category.column] <- factor(nn$set[,nn$category.column])
nn$categories <- levels(nn$set[,nn$category.column])
nn$k <- length(nn$set[1,]) - 1
nn$n <- length(nn$set[,1])
# Scale
return(nn)
}
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