X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=869e7bfc99967f5f4e79b131200c625de39e0a4e;hp=46fb3f33165106d2d4f3a91943b81ac7709b7b74;hb=4cc632c9a1e1d93e9a43a402d1361f23afc50e5e;hpb=1c45d8e4f6fd4209d26709f17a58920218ee828d

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 46fb3f3..869e7bf 100644
--- 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)
@@ -13,36 +14,55 @@
 #' @param Y			 matrix of responses
 #' @param thres	 threshold to consider a coefficient to be equal to 0
 #' @param tau		 threshold to say that EM algorithm has converged
+#' @param ncores Number or cores for parallel execution (1 to disable)
 #'
 #' @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,glambda,X,Y,seuil,tau)
+#'
+selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
+	X,Y,thresh,tau, ncores=3)
 {
-	#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)
+		list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
+	}
+
+	# Pour chaque lambda de la grille, on calcule les coefficients
+	out <-
+		if (ncores > 1)
+			parLapply(cl, glambda, computeCoefs)
+		else
+			lapply(glambda, computeCoefs)
+	if (ncores > 1)
+		parallel::stopCluster(cl)
+
+	# Suppression doublons
+	sha1_array <- lapply(out, digest::sha1)
+	out[ !duplicated(sha1_array) ]
+
 	out
 }