Nothing
##------------------------------------------------------------------------------
#' Initialize empty FTRL Proximal class
#'
#' Online elastic net regression using the FTRL Proximal algorithm for training.
#'
#' This method is intended for setting up a ftrlprox model object before training it using update.
#'
#' @param theta named numeric containing initial coefficients
#' @param levels character vector containing class labels of target label
#' @param lambda regularization term
#' @param alpha mixing parameter, alpha=0 corresponds to L2 regularization and alpha=1 to L1.
#' @param a learning rate parameter
#' @param b learning rate parameter controlling decay, defaults to 1.
#' @param save_loss is to save the loss function during training.
#' @param ... additional args
#' @return ftrlprox model object
#' @author Vilhelm von Ehrenheim
#'
#' @useDynLib FTRLProximal
#' @export
##------------------------------------------------------------------------------
initialize.ftrlprox <- function(theta, levels, lambda, alpha, a, b=1, save_loss=F, ...) {
n <- length(theta)
# Set up an empty object
out <- list(theta = theta,
m = 0,
n = n,
z = numeric(n),
nn = numeric(n),
J = numeric(0),
num_epochs = 1,
a = a,
b = b,
lambda = lambda,
alpha = alpha,
loss = integer(1),
levels = levels)
class(out) <- "ftrlprox"
out
}
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