Fix selectVariables.R

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 46fb3f3..ce7d3b3 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,4 +1,5 @@
 #' selectVariables
+#'
 #' It is a function which construct, for a given lambda, the sets of relevant variables.
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
@@ -19,30 +20,43 @@
 #' @examples TODO
 #'
 #' @export
-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau)
+#'
+selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
+       X,Y,thresh,tau, ncores=1) #ncores==1 ==> no //
 {
-       #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
-       cl = parallel::makeCluster( parallel::detectCores() / 4 )
-       parallel::clusterExport(cl=cl,
-               varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
-               envir=environment())
-       #Pour chaque lambda de la grille, on calcule les coefficients
-       out = parLapply( seq_along(glambda), function(lambdaindex)
+       if (ncores > 1)
+       {
+               cl = parallel::makeCluster(ncores)
+               parallel::clusterExport(cl=cl,
+                       varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
+                       envir=environment())
+       }
+
+       # Calcul pour un lambda
+       computeCoefs <-function(lambda)
        {
+               params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
+
                p = dim(phiInit)[1]
                m = dim(phiInit)[2]
 
-               params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-
                #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$phi[j,,]) > seuil, 1, any ) ]
+                       seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
                })
 
                list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
-       })
-       parallel::stopCluster(cl)
+       }
+
+       # Pour chaque lambda de la grille, on calcule les coefficients
+       out <-
+               if (ncores > 1)
+                       parLapply(cl, seq_along(glambda, computeCoefs)
+               else
+                       lapply(seq_along(glambda), computeCoefs)
+       if (ncores > 1)
+               parallel::stopCluster(cl)
        out
 }

diff --git a/pkg/R/selectiontotale.R b/pkg/R/selectiontotale.R
deleted file mode 100644 (file)
index 042c70b..0000000
--- a/pkg/R/selectiontotale.R
+++ /dev/null
@@ -1,50 +0,0 @@
-#Return a list of outputs, for each lambda in grid: selected,Rho,Pi
-selectiontotale = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,thresh,tau, parallel = FALSE){
-  if (parallel) {
-    require(parallel)
-    cl = parallel::makeCluster( parallel::detectCores() / 4) # <-- ça devrait être un argument
-    parallel::clusterExport(cl=cl,
-                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
-                            envir=environment())
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    out = parLapply(cl,  1:length(glambda), function(lambdaIndex)
-    {
-      params = 
-        EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-      
-      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) {
-        #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$Pi)
-    })
-    parallel::stopCluster(cl)
-  }
-  else {
-    selectedVariables = list()
-    Rho = list()
-    Pi = list()
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    for (lambdaIndex in 1:length(glambda)){
-      print(lambdaIndex)
-      params = 
-        EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-      p = dim(phiInit)[1]
-      m = dim(phiInit)[2]
-      #selectedVariables: list where element j contains vector of selected variables in [1,m]
-      selectedVariables[[lambdaIndex]] = sapply(1:p, function(j) {
-        #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
-        #and finally return the corresponding indices
-        seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
-      })
-      Rho[[lambdaIndex]] = params$Rho
-      Pi[[lambdaIndex]] = params$Pi
-    }
-    list("selected"=selectedVariables,"Rho"=Rho,"Pi"=Pi)
-  }
-}
\ No newline at end of file