#' 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, verbose) { n <- nrow(X) p <- ncol(X) m <- ncol(Y) 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 premiere colonne : on repete (rank.max-rank.min)^(k-1) chaque # chiffre : ca remplit la colonne Dans la deuxieme : on repete # (rank.max-rank.min)^(k-2) chaque chiffre, et on fait ca (rank.max-rank.min)^2 # fois ... Dans la derniere, on repete chaque chiffre une fois, et on fait ca # (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]]$selected relevant <- c() for (j in 1:p) { if (length(selected[[j]]) > 0) relevant <- c(relevant, j) } if (max(rankIndex) < length(relevant)) { phi <- array(0, dim = c(p, m, k)) if (length(relevant) > 0) { res <- EMGrank(S[[lambdaIndex]]$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) { parallel::parLapply(cl, seq_len(length(S) * Size), computeAtLambda) } else { lapply(seq_len(length(S) * Size), computeAtLambda) } if (ncores > 1) parallel::stopCluster(cl) out }