#' NB classificator with utilities
#'
#' For a given email, given utilities, and given
#' parameters of NB classifier, this functions returns the label of the
#' email calculated using NB algorithm
#' @param x the email.
#' @param fit object of class naive-Bayes including the results from
#' training.
#' @return This function returns the label of the email,
#' calculated with NB.
#' @keywords attacks, adversarial learning.
#' @export
#' @examples
#' getNBlabel(x,fit,var)
getNBlabel <- function(x,fit, ut = matrix(c(1,-10,-1,1), nrow = 2, byrow = T, dimnames = list(c("0", "1"), c("0", "1")))){
pSpam = getNBPriors(fit)["1"]
## Case yc == 1
utYcOne = pSpam * ut["1","1"] *
getNBProbabilities(fit, x)[,"1"] +
(1.0 - pSpam) * ut["1","0"] *
getNBProbabilities(fit, x)[,"0"]
## Case yc == 0
utYcZero = pSpam * ut["0","1"]*
getNBProbabilities(fit, x)[,"1"] +
(1.0 - pSpam) * ut["0","0"] *
getNBProbabilities(fit, x)[,"0"]
if(utYcOne > utYcZero){
return("1")
}
else{
return("0")
}
}
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