ite = ite+1
}
-
+
affec = apply(gam, 1, which.max)
return(list("phi"=phi, "rho"=rho, "pi"=pi, "LLF"=LLF, "S"=S, "affec" = affec ))
}
{
if (ncores > 1)
{
- cl = parallel::makeCluster(ncores)
+ cl = parallel::makeCluster(ncores, outfile='')
parallel::clusterExport( cl, envir=environment(),
varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
"tau","S","ncores","verbose") )
out =
if (ncores > 1)
parLapply(cl, 1:length(S), computeAtLambda)
- else
- lapply(1:length(S), computeAtLambda)
+ else
+ lapply(1:length(S), computeAtLambda)
if (ncores > 1)
parallel::stopCluster(cl)
if (ncores > 1)
{
- cl = parallel::makeCluster(ncores)
+ cl = parallel::makeCluster(ncores, outfile='')
parallel::clusterExport( cl, envir=environment(),
varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau",
"Rank","m","phi","ncores","verbose") )
#Pour un groupe de modeles (même k, différents lambda):
llh = matrix(ncol = 2)
for (l in seq_along(models))
- llh = rbind(llh, models[[l]]$llh)
+ llh = rbind(llh, models[[l]]$llh) #TODO: LLF? harmonize between EMGLLF and EMGrank?
LLH = llh[-1,1]
D = llh[-1,2]
k = length(models[[1]]$pi)
tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-
+browser()
modSel = capushe::capushe(data, n)
indModSel <-
if (selecMod == 'DDSE')
{
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())