- # output parameters
- phi = array(0, dim=c(p,m,k,L*Size))
- llh = matrix(0, L*Size, 2) #log-likelihood
- for(lambdaIndex in 1:L)
- {
- # on ne garde que les colonnes actives
- # 'active' sera l'ensemble des variables informatives
- active = A1[,lambdaIndex]
- active = active[-(active==0)]
- if (length(active) > 0)
- {
- for (j in 1:Size)
- {
- res = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi,
- X[,active], Y, tau, Rank[j,])
- llh[(lambdaIndex-1)*Size+j,] =
- c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) )
- phi[active,,,(lambdaIndex-1)*Size+j] = res$phi
+ if (ncores > 1)
+ {
+ cl <- parallel::makeCluster(ncores, outfile = "")
+ parallel::clusterExport(cl, envir = environment(), varlist = c("A1", "Size",
+ "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores",
+ "verbose"))
+ }
+
+ computeAtLambda <- function(index)
+ {
+ lambdaIndex <- RankLambda[index, k + 1]
+ rankIndex <- RankLambda[index, 1:k]
+ if (ncores > 1)
+ require("valse") #workers start with an empty environment
+
+ # 'relevant' will be the set of relevant columns
+ selected <- S[[lambdaIndex]]$selected
+ relevant <- c()
+ for (j in 1:p)
+ {
+ if (length(selected[[j]]) > 0)
+ relevant <- c(relevant, j)
+ }
+ if (max(rankIndex) < length(relevant))
+ {
+ phi <- array(0, dim = c(p, m, k))
+ if (length(relevant) > 0)
+ {
+ res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi,
+ X[, relevant], Y, eps, rankIndex, fast)
+ llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m)))
+ phi[relevant, , ] <- res$phi