R/dcem_test.R

In DCEM: Clustering Big Data using Expectation Maximization Star (EM*) Algorithm

#source("./R/dcem_train.R")
#' dcem_test: Part of DCEM package.
#'
#' For demonstrating the execution on the bundled dataset.
#'
#' @section Details:
#'
#' The dcem_test performs the following steps in order:
#'
#' \enumerate{
#'
#'     \item Read the data from the disk (from the file data/ionosphere_data.csv). The data folder is under the
#'     package installation folder. \item The dataset details can be see by typing \code{\link{ionosphere_data}} in
#'     R-console or at \url{http://archive.ics.uci.edu/ml/datasets/Ionosphere}.
#'
#'      \item Clean the data (by removing the columns). \strong{The data should be cleaned
#'      before use.} Refer \strong{\code{\link{trim_data}}} to see what columns
#'      should be removed and how. The package provides the basic interface for removing
#'      columns.
#'
#'      \item Call the \code{\link{dcem_star_train}} on the cleaned data.
#' }
#'
#' @section Accessing the output parameters:
#'
#' The function dcem_test() calls the \code{\link{dcem_star_train}}.
#' It returns a list of objects as output. This list contains estimated
#' parameters of the Gaussian (posterior probabilities, meu, sigma and prior). The
#' parameters can be accessed as follows where sample_out is the list containing
#' the output:
#'
#'\enumerate{
#'         \item (1) Posterior Probabilities: \strong{sample_out$prob}
#'         A matrix of posterior-probabilities
#'
#'         \item (2) Meu: \strong{meu}
#'
#'         For multivariate data: It is a matrix of meu(s). Each row in
#'         the  matrix corresponds to one meu.
#'
#'         \item (3) Co-variance matrices: \strong{sample_out$sigma}
#'
#'         For multivariate data: List of co-variance matrices for the Gaussian(s).
#'
#'         Standard-deviation: \strong{sample_out$sigma}
#'
#'         For univariate data: Vector of standard deviation for the Gaussian(s))
#'
#'         \item (4) Priors: \strong{sample_out$prior}
#'         A vector of prior.
#'
#'         \item (5) Membership: \strong{sample_out$membership}: A dataframe of
#'         cluster membership for data. Columns numbers are data indices and values
#'         are the assigned clusters.
#'         }
#'
#' @usage
#' dcem_test()
#'
#' @references
#' Parichit Sharma, Hasan Kurban, Mehmet Dalkilic DCEM: An R package for clustering big data via
#' data-centric modification of Expectation Maximization, SoftwareX, 17, 100944 URL
#' https://doi.org/10.1016/j.softx.2021.100944
#'
#' @export

dcem_test <- function()
{

  # Setting the filepath to read from the bundled rda file.
  data_file = system.file("data/ionosphere_data.rda")

  ########
  #### Deprecated code below, not needed anymore
  #### Data is directly loaded using system.file
  # Reading the input file into a dataframe.
  # ionosphere_data = read.csv2(
  #   file = data_file,
  #   sep = ",",
  #   header = FALSE,
  #   stringsAsFactors = FALSE
  # )
  # usethis::use_data(ionosphere_data)
  ##########

  # Cleaning the data by removing the 35th and 2nd column as they contain the labels and 0's respectively.
  ionosphere_data =  trim_data("2, 35", ionosphere_data)

  # Calling the dcem_star_train() function with the cleaned dataset.
  sample_out = dcem_star_train(ionosphere_data)

  # Return the list containing the output parameters.
  return(sample_out)
}