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#' @title GMDH MIA auxiliar functions
#'
#' @description Performs auxiliar tasks to predict.mia
#'
#' @keywords internal
#'
gmdh.mia_1 <- function(X, y, prune) {
fin.1 <- Inf
fin.2 <- 0
lap <- 0
modelos <- vector(mode = "list")
while(fin.1 >= fin.2) {
lap <- lap + 1
message(paste("Layer ", lap, sep = ""))
.var <- combn(c(1:ncol(X)), 2, simplify = TRUE)
message(paste(".................... ", ncol(.var), " neurons ", sep = ""))
mod <- apply(.var, 2, function(.var){fun.N_1(y = y,
x = cbind(X[, .var[1], drop = FALSE],
X[, .var[2], drop = FALSE]))})
names(mod) <- paste(lap, c("."), 1:length(mod), sep = "")
CV.all <- unlist(lapply(mod, function(mod){mod$CV}))
Z <- lapply(mod, predict.neurona, X)
Z <- matrix(data = unlist(Z), nrow = nrow(X), ncol = length(mod))
colnames(Z) <- names(mod)
Z <- fun.filter(Z)
nombres.Z <- colnames(Z)
CV.all <- CV.all[nombres.Z]
ndx <- sort(na.omit(order(CV.all)[1:prune]))
CV.all <- CV.all[ndx, drop = FALSE]
mod <- mod[names(CV.all)]
Z <- Z[, names(CV.all)]
fin.1 <- min(CV.all, na.rm = TRUE)
message(paste(" Error ", fin.1, sep = ""))
lap <- lap + 1
message(paste("Layer ", lap, sep = ""))
.var <- combn(c(1:ncol(Z)), 2, simplify = TRUE)
message(paste(".................... ", ncol(.var), " neurons ", sep = ""))
mod.Z <- apply(.var, 2, function(.var){fun.N_1(y = y,
x = cbind(Z[, .var[1], drop = FALSE],
Z[, .var[2], drop = FALSE]))})
names(mod.Z) <- paste(lap, c("."), 1:length(mod.Z), sep = "")
CV.all <- unlist(lapply(mod.Z, function(mod.Z){mod.Z$CV}))
X <- lapply(mod.Z, predict.neurona, Z)
X <- matrix(data = unlist(X), ncol = length(mod.Z))
colnames(X) <- names(mod.Z)
X <- fun.filter(X)
nombres.X <- colnames(X)
CV.all <- CV.all[nombres.X]
ndx <- sort(na.omit(order(CV.all)[1:prune]))
CV.all <- CV.all[ndx, drop = FALSE]
mod.Z <- mod.Z[names(CV.all)]
X <- X[, names(CV.all)]
modelos[[length(modelos) + 1]] <- mod
modelos[[length(modelos) + 1]] <- mod.Z
class(modelos) <- "mia"
fin.2 <- min(CV.all, na.rm = TRUE)
message(paste(" Error ", fin.2, sep = ""))
ifelse(fin.2 >= fin.1, return(modelos), NA)
}
}
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