without parallel for selectiontotale.R (problem with parallel)

[valse.git] / R / selectiontotale.R

diff --git a/R/selectiontotale.R b/R/selectiontotale.R
index 673bc3b..5b0112c 100644 (file)
--- a/R/selectiontotale.R
+++ b/R/selectiontotale.R
@@ -1,25 +1,50 @@
 #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,seuil,tau)
-{
-       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( 1:L, 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$phi[j,,]) > seuil, 1, any ) ]
-               })
-
-               list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
-       })
-       parallel::stopCluster(cl)
-}
+selectiontotale = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,thresh,tau, parallel = FALSE){
+  if (parallel) {
+    require(parallel)
+    cl = parallel::makeCluster( parallel::detectCores() / 4) # <-- ça devrait être un argument
+    parallel::clusterExport(cl=cl,
+                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
+                            envir=environment())
+    #Pour chaque lambda de la grille, on calcule les coefficients
+    out = parLapply(cl,  1:length(glambda), function(lambdaIndex)
+    {
+      params = 
+        EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
+      
+      p = dim(phiInit)[1]
+      m = dim(phiInit)[2]
+      #selectedVariables: list where element j contains vector of selected variables in [1,m]
+      selectedVariables = lapply(1:p, function(j) {
+        #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
+        #and finally return the corresponding indices
+        seq_len(m)[ apply( abs(params$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]] = 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$phi[j,,]) > thresh, 1, any ) ]
+      })
+      Rho[[lambdaIndex]] = params$Rho
+      Pi[[lambdaIndex]] = params$Pi
+    }
+    list("selected"=selectedVariables,"Rho"=Rho,"Pi"=Pi)
+  }
+}
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