EC_time<-function(X, Y=rep(NA, nrow(X)), nvars, kmin, kmax, ncores){
start.time<-Sys.time()
wt<-X[,1]
X<-X[,-1]
col_cat<-seq(1,ncol(X),1)
combn<-utils::combn(c(1:ncol(X)),nvars)
col_indx=matrix(c("NULL"), ncol(combn), ncol(X), byrow=FALSE)
for (i in 1:ncol(combn)){
col_indx[i,]<-t(is.element(col_cat, combn[,i]))
col_indx[i,][col_indx[i,]==FALSE]<-NA
colnames(col_indx)=colnames(X)
}
doParallel::registerDoParallel(cores=ncores)
ASW<-matrix(nrow=nrow(col_indx), ncol=(kmax-kmin+1))
#' @import foreach
#' @import doParallel
ASW<-foreach::foreach (i=1:ncores, .combine='rbind') %dopar% {
combi<-X[,!is.na(col_indx[i,])]
if(any(!is.na(Y))){
combi<-cbind(Y, combi)
}
combi_mat<-data.matrix(combi)
parD<-parDist(combi_mat, method = "hamming")
wcKMR<-WeightedCluster::wcKMedRange(parD, kvals=(kmin), weights=wt)
ASW[i,]<-wcKMR$stats[,5]
rm(combi)
rm(dizzy)
rm(wcKMR)
return(ASW[i,])
}
ASW_max<-max(ASW, na.rm=TRUE)
ASW_max_model<-which(ASW == ASW_max, arr.ind=TRUE)
model<-list()
Assets<-list()
clust<-list()
K<-list()
for (a in 1:nrow(ASW_max_model)){
model[[a]]<-ASW_max_model[a,1]
Assets[[a]]<-which( !is.na(col_indx[model[[a]],]), arr.ind=TRUE)
Assets[[a]]<-names(Assets[[a]])
if (any(!is.na(Y))){
Assets[[a]]<-c(deparse(substitute(Y)), Assets[[a]])
}
clust[[a]]<-ASW_max_model[a,2]
K[[a]]<-kmin-1+clust[[a]]
}
results<-list()
results$ASW_max<-ASW_max
results$Assets<-Assets
results$K<-K
end.time<-Sys.time()
time<-end.time - start.time
fulltime<-(as.numeric(time)*choose(ncol(X), nvars))/(ncores*60*60)
return(fulltime)
#' @export
}
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