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#' Useful example for illustrating generative models based on MNIST data
#'
#' We only include a randomly selected set of 1s, 2s and 7s along with the two predictors based on the
#' proportion of dark pixels in the upper left and lower right quadrants respectively. The dataset is divided into
#' training and test sets.
#'
#'
#' \itemize{
#' \item train. A data frame containing training data: labels and predictors.
#' \item test. A data frame containing test data: labels and predictors.
#' \item index_train. The index of the original mnist training data used for the training set.
#' \item index_test. The index of the original mnist test data used for the test set.
#' }
#'
#' @seealso [read_mnist(), mnist_27]
#'
#' @docType data
#'
#' @usage mnist_127
#'
#' @format An object of class \code{list}.
#'
#' @keywords datasets
#'
#' @references Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.
#'
#'
#' @examples
#' with(mnist_127$train, plot(x_1, x_2, col = as.numeric(y)))
#'
"mnist_127"
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