X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=99959cac1b83c789e03e84853a6e2018f8619f98;hp=cdc0ec00f672d534d65c8a9b5dfb951b79a5e468;hb=3921ba9b5ea85bcc190245ac7da9ee9da1658b9f;hpb=ca277ac5ab51fef149014eb5e4610403fdb3227b

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index cdc0ec0..99959ca 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,6 +1,6 @@
-#' selectVariables 
+#' selectVariables
 #'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' For a given lambda, construct the sets of relevant variables for each cluster.
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
@@ -15,37 +15,42 @@
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
 #' @param eps   threshold to say that EM algorithm has converged
 #' @param ncores Number or cores for parallel execution (1 to disable)
+#' @param fast boolean to enable or not the C function call
 #'
 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
 #'
-#' @examples TODO
-#'
 #' @export
-#'
-selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
   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", 
+    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, 
+    params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
       X, Y, eps, fast)
 
-    p <- dim(phiInit)[1]
-    m <- dim(phiInit)[2]
+    p <- ncol(X)
+    m <- ncol(Y)
 
     # selectedVariables: list where element j contains vector of selected variables
     # in [1,m]
     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$phi[j, , ]) > thresh, 1, any)]
+      if (m>1) {
+        seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+      } else {
+        if (any(params$phi[j, , ] > thresh))
+          1
+        else
+          numeric(0)
+      }
     })
 
     list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
@@ -58,11 +63,11 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
     } else {
       lapply(glambda, computeCoefs)
     }
-  if (ncores > 1) 
+  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
+
+  print(out) #DEBUG TRACE
+  # Suppress models which are computed twice
   # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)