X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=bfe4042d1ec639173b38bd65ac9cb113c186b564;hp=fe0688c5989954eca72c7dfbb21afbebde5f8f1d;hb=a3cbbaea1cc3c107e5ca62ed1ffe7b9499de0a91;hpb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index fe0688c..bfe4042 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -23,52 +23,52 @@
 #' @export
 #'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
-  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
-  {
-  if (ncores > 1)
-  {
+  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
+{
+  if (ncores > 1) {
     cl <- parallel::makeCluster(ncores, outfile = "")
     parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit", 
       "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
   }
-  
+
   # Computation for a fixed lambda
   computeCoefs <- function(lambda)
   {
     params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
       X, Y, eps, fast)
-    
+
     p <- dim(phiInit)[1]
     m <- dim(phiInit)[2]
-    
+
     # selectedVariables: list where element j contains vector of selected variables
     # in [1,m]
-    selectedVariables <- lapply(1:p, function(j)
-    {
+    selectedVariables <- lapply(1:p, function(j) {
       # from boolean matrix mxk of selected variables obtain the corresponding boolean
       # m-vector, and finally return the corresponding indices
       seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
     })
-    
+
     list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
   }
-  
+
   # For each lambda in the grid, we compute the coefficients
-  out <- if (ncores > 1) 
-    parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, glambda, computeCoefs)
+    } else {
+      lapply(glambda, computeCoefs)
+    }
   if (ncores > 1) 
     parallel::stopCluster(cl)
   # Suppress models which are computed twice En fait, ca ca fait la comparaison de
   # tous les parametres On veut juste supprimer ceux qui ont les memes variables
-  # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
-  # duplicated(sha1_array) ]
+  # sélectionnées
+  # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)
   ind_uniq <- which(!ind_dup)
   out2 <- list()
   for (l in 1:length(ind_uniq))
-  {
     out2[[l]] <- out[[ind_uniq[l]]]
-  }
   out2
 }