X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoRank.R;h=dc88f676f1ed8ca11e0ec5b91013e304226df309;hp=5da26e3594573ee7df9a31e9fe08c19e9eb121f2;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=43d76c49d2f98490abc782c7e8a8b94baee40247

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
deleted file mode 100644
index 5da26e3..0000000
--- a/pkg/R/constructionModelesLassoRank.R
+++ /dev/null
@@ -1,95 +0,0 @@
-#' 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
-}