Nothing
InternalMMPC <- function(target, dataset, max_k, threshold, test=NULL, ini=NULL, wei=NULL, user_test=NULL, hash=FALSE,
varsize, stat_hash, pvalue_hash, targetID, ncores) {
#get the current time
#######################################################################################
#univariate feature selection test
if ( is.null(ini) ) {
univariateModels <- univregs(target = target, dataset = dataset, targetID = targetID, test = test, user_test = user_test, wei = wei, ncores = ncores)
} else univariateModels = ini
pvalues <- univariateModels$pvalue;
stats <- univariateModels$stat;
#if we dont have any associations , return
if ( min(pvalues, na.rm = TRUE) > threshold ) {
#cat('No associations!');
results <- NULL;
results$selectedVars = c();
class(results$selectedVars) = "numeric";
results$selectedVarsOrder = c();
class(results$selectedVarsOrder) = "numeric";
results$hashObject = NULL;
class(results$hashObject) = 'list';
class(results$univ) = 'list';
results$pvalues = pvalues;
results$stats = stats;
results$univ = univariateModels
results$max_k = max_k;
results$threshold = threshold;
results$n.tests <- length(stats)
return(results);
}
#Initialize the data structs
selectedVars = numeric(varsize);
selectedVarsOrder = numeric(varsize);
#select the variable with the highest association
selectedVar = which.min(pvalues)
selectedVars[selectedVar] = 1;
selectedVarsOrder[selectedVar] = 1; #CHANGE
#remaining variables to be considered
remainingVars = numeric(varsize) + 1;
remainingVars[selectedVar] = 0;
remainingVars[pvalues > threshold] = 0;
if (targetID > 0) remainingVars[targetID] = 0;
################ main MMPC loop ################
#loop until there are not remaining vars
loop = any( as.logical(remainingVars) );
#rep = 1;
while (loop) {
max_min_results = max_min_assoc(target, dataset, test, wei, threshold, max_k, selectedVars, pvalues, stats, remainingVars , univariateModels, selectedVarsOrder, hash=hash, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
selectedVar = max_min_results$selected_var;
selectedPvalue = max_min_results$selected_pvalue;
remainingVars = max_min_results$remainingVars;
pvalues = max_min_results$pvalues;
stats = max_min_results$stats;
stat_hash=max_min_results$stat_hash;
pvalue_hash=max_min_results$pvalue_hash;
#if the selected variable is associated with target , add it to the selected variables
if ( selectedPvalue <= threshold ) {
#print(paste("rep: ",rep,", selected var: ",selectedVar,", pvalue = ",exp(selectedPvalue)))
#rep = rep + 1;
selectedVars[selectedVar] = 1;
selectedVarsOrder[selectedVar] = max(selectedVarsOrder) + 1;
remainingVars[selectedVar] = 0;
}
loop = any( as.logical(remainingVars) );
}
selectedVarsOrder[which(!selectedVars)] = varsize;#
numberofSelectedVars = sum(selectedVars);#
selectedVarsOrder = sort(selectedVarsOrder);#
# selectedVars = selectedVarsOrder[1:numberofSelectedVars];
# #queues correctness
# all_queues = queues
# queues = queues[which(selectedVars==1)];
# queues <- lapply(1:length(queues) , function(i){queues[[i]] = unique(queues[[i]]);});
#adjusting the results
if (targetID > 0) {
toAdjust <- which(selectedVars > targetID)
selectedVars[toAdjust] = selectedVars[toAdjust] + 1
}
results = NULL;
results$selectedVars = which( selectedVars == 1 )
svorder = sort(pvalues[results$selectedVars], index.return = TRUE)
svorder = results$selectedVars[svorder$ix]
results$selectedVarsOrder = svorder
hashObject = NULL
hashObject$stat_hash = stat_hash
hashObject$pvalue_hash = pvalue_hash
results$hashObject = hashObject
class(results$hashObject) = 'list'
results$pvalues = pvalues;
results$stats = stats;
results$univ = univariateModels
results$max_k = max_k
results$threshold = threshold
results$n.tests <- length(stats) + length( hashObject$stat_hash )
return(results)
}
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