- if (ncores > 1)
- require("valse") #// nodes start with an empty 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 = S[[lambda]]$selected
- # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
- col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-
- if (length(col.sel) == 0)
- {return (NULL)} else {
-
- # lambda == 0 because we compute the EMV: no penalization here
- res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
- X[,col.sel],Y,tau)
-
- # Eval dimension from the result + selected
- phiLambda2 = res_EM$phi
- rhoLambda = res_EM$rho
- piLambda = res_EM$pi
- phiLambda = array(0, dim = c(p,m,k))
- for (j in seq_along(col.sel))
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
-
- dimension = 0
- for (j in 1:p)
- {
- b = setdiff(1:m, sel.lambda[[j]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ?
- ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
- if (length(b) > 0)
- phiLambda[j,b,] = 0.0
- dimension = dimension + sum(sel.lambda[[j]]!=0)
- }
-
- # Computation of the loglikelihood
- densite = vector("double",n)
- for (r in 1:k)
+ if (ncores > 1)
+ require("valse") #nodes start with an empty environment
+
+ if (verbose)
+ print(paste("Computations for lambda=", lambda))
+
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ k <- length(piInit)
+ sel.lambda <- S[[lambda]]$selected
+ # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+ col.sel <- which(sapply(sel.lambda, length) > 0) #if list of selected vars
+ if (length(col.sel) == 0)
+ return(NULL)
+
+ # lambda == 0 because we compute the EMV: no penalization here
+ res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
+ mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+
+ # Eval dimension from the result + selected
+ phiLambda2 <- res$phi
+ rhoLambda <- res$rho
+ piLambda <- res$pi
+ phiLambda <- array(0, dim = c(p, m, k))
+ for (j in seq_along(col.sel))
+ phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
+ dimension <- length(unlist(sel.lambda))
+
+ ## Affectations
+ Gam <- matrix(0, ncol = length(piLambda), nrow = n)
+ for (i in 1:n)
+ {
+ for (r in 1:length(piLambda))