+++ /dev/null
-#' 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 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]]$selected
- relevant <- c()
- for (j in 1:p)
- {
- if (length(selected[[j]]) > 0)
- relevant <- c(relevant, j)
- }
- if (max(rankIndex) < length(relevant))
- {
- phi <- array(0, dim = c(p, m, k))
- if (length(relevant) > 0)
- {
- res <- EMGrank(S[[lambdaIndex]]$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) {
- parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
- } else {
- lapply(seq_len(length(S) * Size), computeAtLambda)
- }
-
- if (ncores > 1)
- parallel::stopCluster(cl)
-
- out
-}