- if (parallel) {
- #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
- cl = parallel::makeCluster( parallel::detectCores() / 4 )
- parallel::clusterExport(cl=cl,
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(lambda)
- {
- n = dim(X)[1]
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- k = dim(phiInit)[3]
-
- #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
- #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
- res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
-
- #comment évaluer la dimension à partir du résultat et de [not]selected ?
- #dimension = ...
-
- #on veut calculer la vraisemblance avec toutes nos estimations
- densite = vector("double",n)
- for (r in 1:k)
- {
- delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
- densite = densite + pi[r] *
- det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
- }
- llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
- list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
- })
- parallel::stopCluster(cl)
- out
- }
- else {
- #Pour chaque lambda de la grille, on calcule les coefficients
- n = dim(X)[1]
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- k = dim(phiInit)[3]
- L = length(selected)
- phi = list()
+ if (ncores > 1)
+ {
+ cl = parallel::makeCluster(ncores)
+ parallel::clusterExport( cl, envir=environment(),
+ varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","seuil",
+ "tau","selected","ncores","verbose") )
+ }
+
+ # Individual model computation
+ computeAtLambda <- function(lambda)
+ {
+ if (ncores > 1)
+ require("valse") #// nodes start with an ampty environment
+
+ if (verbose)
+ print(paste("Computations for lambda=",lambda))
+
+ n = dim(X)[1]
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+ k = dim(phiInit)[3]
+
+ sel.lambda = selected[[lambda]]
+# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+ col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
+
+ if (length(col.sel) == 0)
+ return (NULL)
+
+ # lambda == 0 because we compute the EMV: no penalization here
+ res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+ X[,col.sel],Y,tau)
+
+ # Eval dimension from the result + selected
+ phiLambda2 = res_EM$phi
+ rhoLambda = res_EM$rho
+ piLambda = res_EM$pi