-#' 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]]$selected
- relevant = c()
- for (j in 1:p){
- if (length(selected[[j]])>0){
- relevant = c(relevant,j)
- }
- }
- if (max(rankIndex)<length(relevant)){
- phi = array(0, dim=c(p,m,k))
- if (length(relevant) > 0)
- {
- res = EMGrank(S[[lambdaIndex]]$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
-}