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#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)
}
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