#' constructionModelesLassoRank #' #' Construct a collection of models with the Lasso-Rank procedure. #' #' @param S output of selectVariables.R #' @param k number of components #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 #' @param X matrix of covariates (of size n*p) #' @param Y matrix of responses (of size n*m) #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 #' @param rank.min integer, minimum rank in the low rank procedure, by default = 1 #' @param rank.max integer, maximum rank in the low rank procedure, by default = 5 #' @param ncores Number of cores, by default = 3 #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces #' #' @return a list with several models, defined by phi, rho, pi, llh #' #' @export constructionModelesLassoRank = function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, ncores, fast=TRUE, verbose=FALSE) { n = dim(X)[1] p = dim(X)[2] m = dim(Y)[2] L = length(S) # Possible interesting ranks deltaRank = rank.max - rank.min + 1 Size = deltaRank^k RankLambda = matrix(0, nrow=Size*L, ncol=k+1) for (r in 1:k) { # On veut le tableau de toutes les combinaisons de rangs possibles, et des lambdas # Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque chiffre : # ça remplit la colonne # Dans la deuxieme : on répète (rank.max-rank.min)^(k-2) chaque chiffre, # et on fait ça (rank.max-rank.min)^2 fois # ... # Dans la dernière, on répète chaque chiffre une fois, # et on fait ça (rank.min-rank.max)^(k-1) fois. RankLambda[,r] = rep(rank.min + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r)), each = L) } RankLambda[,k+1] = rep(1:L, times = Size) 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) 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 } list("llh"=llh, "phi"=phi, "pi" = S[[lambdaIndex]]\$Pi, "rho" = S[[lambdaIndex]]\$Rho) } } #For each lambda in the grid we compute the estimators out = if (ncores > 1) parLapply(cl, seq_len(length(S)*Size), computeAtLambda) else lapply(seq_len(length(S)*Size), computeAtLambda) if (ncores > 1) parallel::stopCluster(cl) out }