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#' Calculate a predicted value of GRNN
#'
#' The function \code{grnn.predone} calculates a predicted value of GRNN based on an input vector
#'
#' @param net The GRNN object generated by grnn.fit()
#' @param x The vector of input predictors
#' @param type A scalar, 1 for euclidean distance and 2 for manhattan distance
#'
#' @return A scalar of the predicted value
#'
#' @references
#' Donald Specht. (1991). A General Regression Neural Network.
#'
#' @seealso \code{\link{grnn.fit}}
#'
#' @examples
#' data(iris, package = "datasets")
#' Y <- ifelse(iris[, 5] == "setosa", 1, 0)
#' X <- scale(iris[, 1:4])
#' gnet <- grnn.fit(x = X, y = Y)
#' for (i in seq(5)) print(grnn.predone(gnet, X[i, ]))
grnn.predone <- function(net, x, type = 1) {
if (class(net) != "General Regression Neural Net") stop("net needs to be a GRNN object.", call. = F)
### CHECK INPUT X VECTOR ###
if (is.vector(x) == F) stop("x needs to be a vector.", call. = F)
if (anyNA(x) == T) stop("NA found in x.", call. = F)
if (length(x) != ncol(net$x)) stop("x dimension is not consistent with grnn.", call. = F)
### CHECK INPUT TYPE (CURRENTLY SUPPORTING 1 / 2) ###
if (!(type %in% c(1, 2))) stop("the type is not supported.", call. = F)
# xl <- split(net$x, seq(nrow(net$x)))
xl <- matrix(rep(x, length(net$y)), nrow = length(net$y), byrow = TRUE)
if (type == 1) {
### EUCLIDEAN DISTANCE BY DEFAULT ###
num <- sum(net$w * net$y * exp(-(rowSums((xl - net$x) ^ 2)) / (2 * (net$sigma ^ 2))))
den <- sum(net$w * exp(-(rowSums((xl - net$x) ^ 2)) / (2 * (net$sigma ^ 2))))
# num <- sum(net$w * net$y * exp(-Reduce(c, lapply(xl, function(xi) sum((x - xi) ^ 2))) / (2 * (net$sigma ^ 2))))
# den <- sum(net$w * exp(-Reduce(c, lapply(xl, function(xi) sum((x - xi) ^ 2))) / (2 * (net$sigma ^ 2))))
} else if (type == 2) {
### MANHATTAN DISTANCE ###
num <- sum(net$w * net$y * exp(-(rowSums(abs(xl - net$x))) / net$sigma))
den <- sum(net$w * exp(-(rowSums(abs(xl - net$x))) / net$sigma))
# num <- sum(net$w * net$y * exp(-Reduce(c, lapply(xl, function(xi) sum(abs(x - xi)))) / net$sigma))
# den <- sum(net$w * exp(-Reduce(c, lapply(xl, function(xi) sum(abs(x - xi)))) / net$sigma))
}
return(num / den)
}
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