#' @eval get_description('split_data')

 #' @export split_data

 #' @examples

 #' M = split_data(p_train=0.75)

 #'

 split_data = function(p_train,...) {

     out=struct::new_struct('split_data',

         p_train=p_train,

         ...)

     return(out)
 }
 
 
 .split_data<-setClass(

     "split_data",

     contains = c('model'),

     slots=c(
         p_train='entity',

         training='entity',
         testing='entity'

     ),

     prototype=list(
         name = 'Split data',

         description = paste0('The data matrix is divided into two subsets.',
         'A predefined proportion of the samples are randomly selected for a ',
         'training set, and the remaining samples are used for the test set.'),

         type = 'processing',
         predicted = 'testing',

         .params=c('p_train'),

         .outputs=c('training','testing'),

         p_train=entity(name = 'Proportion in training set',

             description = paste0('The proportion of samples selected for the ',
             'training set.'),

             value = 0.75,
             type='numeric'),

         training=entity(name = 'A DatasetExperiment of training data',

             description = 'A DatasetExperiment object containing samples selected for the training set.',
             type='DatasetExperiment',
             value=DatasetExperiment()

         ),

         testing=entity(name = 'A DatasetExperiment of data for testing',

             description = 'A DatasetExperiment object containing samples selected for the testing set.',
             type='DatasetExperiment',
             value=DatasetExperiment()

         )
     )

 )
 
 #' @export

 #' @template model_apply

 setMethod(f="model_apply",
     signature=c("split_data","DatasetExperiment"),

     definition=function(M,D) {

         opt=param_list(M)

         # number of samples

         nMax=nrow(D$data)

         # number in the training set

         n=floor(nMax*opt$p_train)

         # select a random subset of the data for training
         in_training=sample(x=1:nMax,size = n, replace=FALSE,prob=NULL)

         training=DatasetExperiment(data=D$data[in_training,,drop=FALSE],
             sample_meta=D$sample_meta[in_training,,drop=FALSE],

             variable_meta=D$variable_meta,
             name=c(D$name,'(Training set)'),
             description=c(D$description,'A subset of the data has been selected as a training set'))

         testing=DatasetExperiment(data=D$data[-in_training,,drop=FALSE],
             sample_meta=D$sample_meta[-in_training,,drop=FALSE],

             variable_meta=D$variable_meta,
             name=c(D$name,'(Testing set)'),
             description=c(D$description,'A subset of the data has been selected as a test set'))

         output_value(M,'training')=training
         output_value(M,'testing')=testing

         return(M)
     }

 )