-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau)
-{
- #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","glambda","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(lambdaindex)
- {
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
-
- params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-
- #selectedVariables: list where element j contains vector of selected variables in [1,m]
- 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$phi[j,,]) > seuil, 1, any ) ]
- })
-
- list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
- })
- parallel::stopCluster(cl)
- out
+#'
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
+ glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+ {
+ if (ncores > 1)
+ {
+ cl <- parallel::makeCluster(ncores, outfile = "")
+ parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
+ "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
+ }
+
+ # Computation for a fixed lambda
+ computeCoefs <- function(lambda)
+ {
+ params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
+ X, Y, eps, fast)
+
+ p <- dim(phiInit)[1]
+ m <- dim(phiInit)[2]
+
+ # selectedVariables: list where element j contains vector of selected variables
+ # in [1,m]
+ selectedVariables <- lapply(1:p, function(j)
+ {
+ # from boolean matrix mxk of selected variables obtain the corresponding boolean
+ # m-vector, and finally return the corresponding indices
+ seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+ })
+
+ list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
+ }
+
+ # For each lambda in the grid, we compute the coefficients
+ out <- if (ncores > 1)
+ parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+ if (ncores > 1)
+ parallel::stopCluster(cl)
+ # Suppress models which are computed twice En fait, ca ca fait la comparaison de
+ # tous les parametres On veut juste supprimer ceux qui ont les memes variables
+ # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
+ # duplicated(sha1_array) ]
+ selec <- lapply(out, function(model) model$selected)
+ ind_dup <- duplicated(selec)
+ ind_uniq <- which(!ind_dup)
+ out2 <- list()
+ for (l in 1:length(ind_uniq))
+ {
+ out2[[l]] <- out[[ind_uniq[l]]]
+ }
+ out2