+#' constructionModelesLassoMLE
+#'
+#' TODO: description
+#'
+#' @param ...
+#'
+#' @return ...
+#'
+#' export
+constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+ gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
+{
+ 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
+ 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])
+ if (length(b) > 0)
+ phiLambda[j,b,] = 0.0
+ dimension = dimension + sum(sel.lambda[,j]!=0)
+ }
+
+ # on veut calculer la vraisemblance avec toutes nos estimations
+ densite = vector("double",n)
+ for (r in 1:k)
+ {
+ delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+ densite = densite + piLambda[r] *
+ det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+ }
+ llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
+ list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
+ }
+
+ #Pour chaque lambda de la grille, on calcule les coefficients
+ out =
+ if (ncores > 1)
+ parLapply(cl, glambda, computeAtLambda)
+ else
+ lapply(glambda, computeAtLambda)
+
+ if (ncores > 1)
+ parallel::stopCluster(cl)
+
+ out
+}