X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoRank.R;h=eeab8f36eea1f059e7ba1571ab62dbab7403b1b5;hp=dc6bcc1efe1331771dd4eb7d1555b29b35a8f797;hb=430a98439a49281c4b17801c95da3e6d9ef88488;hpb=95dc88bfe8495527cebe7f64db4e75a69773f2e5

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index dc6bcc1..eeab8f3 100644
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -1,7 +1,7 @@
 #' 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
@@ -14,50 +14,48 @@
 #' @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)
-{
+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)
+    # 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"))
+    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]]$selected
     relevant <- c()
@@ -73,24 +71,27 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       phi <- array(0, dim = c(p, m, k))
       if (length(relevant) > 0)
       {
-        res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, 
-          X[, relevant], Y, eps, rankIndex, fast)
-        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + 
-          m)))
+        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)
-
+  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
 }