R/rShinyDatasetDocumentation.R

In SpencerButt/IDPS-LAAD: Intrusion Detection Prevention System Log Autoencoder Anomaly Detector

#' Default hyperparameter experimental test design for a single-hidden layer undercomplete 
#' autoencoder neural netowrk.
#'
#'@section Information: A .rda dataset containing the factors and levels of 600-test trial designed experiment
#' generated in JMP Pro v12.1. The designed experiment is a flexible space filling design 
#' for 9 test design factors.
#' 
#' @format A dataset with 600 rows and 12 features; 9-test design features, 1-response 
#' variable feature, and 1-design point index feature
#' 
#' \itemize{
#'   \item{\strong{Subset_Split: }}{Categorical 3-level factor; describes the subset to use 
#'   in the Autoencoder}
#'   \item{\strong{Design_Point: }}{An index of each test point}
#'   \item{\strong{Activatino_Function: }}{Categroical 2-level factor; describes the activation 
#'   function within each neuron in the neural network}
#'   \item{\strong{Input_DO_Rate: }}{Continuous multi-level factor; describes the Dropout 
#'   rate of the input layer neurons}
#'   \item{\strong{Hidden_DO_Rate: }}{Continuous multi-level factor; describes the Dropout 
#'   rate of the hidden layer neurons}
#'   \item{\strong{Initial_Weight_Distribution: }}{Categorical 3-level factor; describes the 
#'   distribution by which the initial weights of the autoencoder neural network 
#'   are generated}
#'   \item{\strong{Data_Scale: }}{Categorical 3-level factor; describes the range to which 
#'   the training and test subset data is scaled.}
#'   \item{\strong{Rho: }}{Continuous multi-level factor; describes the value of the rho parameter 
#'   for the ADADELTA learning procedure}
#'   \item{\strong{Epsilon: }}{Continuous multi-level factor; describes the value of the epsilon 
#'   parameter for the ADADELTA learning procedure}
#'   \item{\strong{Shuffle_Train_Data: }}{Categorical 2-level factor; boolean value indicating 
#'   if the training data should be randomly shuffled during neural network training}
#'   \item{\strong{Y: }}{Placeholder for the response value measurement}
#' }
"testDesignShiny"