#' constructionModelesLassoMLE #' #' TODO: description #' #' @param ... #' #' @return ... #' #' export constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE) { if (ncores > 1) { cl = parallel::makeCluster(ncores, outfile='') parallel::clusterExport( cl, envir=environment(), varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh", "tau","S","ncores","verbose") ) } # Individual model computation computeAtLambda <- function(lambda) { 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]]$selected # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars if (length(col.sel) == 0) return (NULL) # lambda == 0 because we compute the EMV: no penalization here res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0, X[,col.sel], Y, tau, fast) # Eval dimension from the result + selected phiLambda2 = res$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],,] = phiLambda2[j,,] dimension = length(unlist(sel.lambda)) # Computation of the loglikelihood 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(-diag(tcrossprod(delta))/2.0) } llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 ) list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda) } # For each lambda, computation of the parameters out = if (ncores > 1) parLapply(cl, 1:length(S), computeAtLambda) else lapply(1:length(S), computeAtLambda) if (ncores > 1) parallel::stopCluster(cl) out }