#' @param Y matrix of responses
#' @param thres threshold to consider a coefficient to be equal to 0
#' @param tau threshold to say that EM algorithm has converged
+#' @param ncores Number or cores for parallel execution (1 to disable)
#'
#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
#'
#' @export
#'
selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
- X,Y,thresh,tau, ncores=1) #ncores==1 ==> no //
+ X,Y,thresh,tau, ncores=3)
{
if (ncores > 1)
{
- cl = parallel::makeCluster(ncores)
+ cl = parallel::makeCluster(ncores, outfile='')
parallel::clusterExport(cl=cl,
varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
envir=environment())
}
# Calcul pour un lambda
- computeCoefs <-function(lambda)
+ computeCoefs <- function(lambda)
{
params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
m = dim(phiInit)[2]
#selectedVariables: list where element j contains vector of selected variables in [1,m]
- selectedVariables = sapply(1:p, function(j) { ## je me suis permise de changer le type,
- ##une liste de liste ca devenait compliqué je trouve pour choper ce qui nous intéresse
+ selectedVariables = lapply(1:p, function(j) {
#from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
#and finally return the corresponding indices
- #seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
- c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ],
- rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))
+ seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
})
list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
# Pour chaque lambda de la grille, on calcule les coefficients
out <-
- if (ncores > 1){
- parLapply(cl, seq_along(glambda, computeCoefs))}
- else lapply(seq_along(glambda), computeCoefs)
- if (ncores > 1){
- parallel::stopCluster(cl)}
+ if (ncores > 1)
+ parLapply(cl, glambda, computeCoefs)
+ else
+ lapply(glambda, computeCoefs)
+ if (ncores > 1)
+ parallel::stopCluster(cl)
+
+ # Suppression doublons
+ sha1_array <- lapply(out, digest::sha1)
+ out[ !duplicated(sha1_array) ]
+
out
}