X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=6c37751b465d6a6d635f0029a8a5b9f94b201966;hp=d2bb9a5557904a0ab0b10b7ebad7e678b7b603ec;hb=2279a641f2bee1db586e7ab1e13726d111d5daaf;hpb=4cc632c9a1e1d93e9a43a402d1361f23afc50e5e

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index d2bb9a5..6c37751 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,90 +1,86 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
-                                       X,Y,seuil,tau,selected, parallel = FALSE)
+#' constructionModelesLassoMLE
+#'
+#' TODO: description
+#'
+#' @param ...
+#'
+#' @return ...
+#'
+#' export
+constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+	gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
 {
-  if (parallel) {
-    #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","X","Y","seuil","tau"),
-                            envir=environment())
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    out = parLapply( seq_along(glambda), function(lambda)
-    {
-      n = dim(X)[1]
-      p = dim(phiInit)[1]
-      m = dim(phiInit)[2]
-      k = dim(phiInit)[3]
-      
-      #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
-      #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
-      res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
-      
-      #comment évaluer la dimension à partir du résultat et de [not]selected ?
-      #dimension = ...
-      
-      #on veut calculer la vraisemblance avec toutes nos estimations
-      densite = vector("double",n)
-      for (r in 1:k)
-      {
-        delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
-        densite = densite + pi[r] *
-          det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
-      list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
-    })
-    parallel::stopCluster(cl)
-    out
-  }
-  else {
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    n = dim(X)[1]
-    p = dim(phiInit)[1]
-    m = dim(phiInit)[2]
-    k = dim(phiInit)[3]
-    L = length(selected)
-    phi = list()
+  if (ncores > 1)
+	{
+    cl = parallel::makeCluster(ncores)
+    parallel::clusterExport( cl, envir=environment(),
+			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","seuil",
+			"tau","selected","ncores","verbose") )
+	}
+
+	# Individual model computation
+	computeAtLambda <- function(lambda)
+	{
+		if (ncores > 1)
+			require("valse") #// nodes start with an ampty environment
+
+    if (verbose)
+			print(paste("Computations for lambda=",lambda))
+
+		n = dim(X)[1]
+		p = dim(phiInit)[1]
+		m = dim(phiInit)[2]
+		k = dim(phiInit)[3]
+
+		sel.lambda = selected[[lambda]]
+#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
+
+		if (length(col.sel) == 0)
+			return (NULL)
+
+		# lambda == 0 because we compute the EMV: no penalization here
+		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+			X[,col.sel],Y,tau)
+		
+		# Eval dimension from the result + selected
+		phiLambda2 = res_EM$phi
+		rhoLambda = res_EM$rho
+		piLambda = res_EM$pi
     phiLambda = array(0, dim = c(p,m,k))
-    rho = list()
-    pi = list()
-    llh = list()
-    
-    out = lapply( seq_along(selected), function(lambda)
-    {
-      print(lambda)
-      sel.lambda = selected[[lambda]]
-      col.sel = which(colSums(sel.lambda)!=0)
-      if (length(col.sel)>0){
-        res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
-        phiLambda2 = res_EM$phi
-        rhoLambda = res_EM$rho
-        piLambda = res_EM$pi
-        for (j in 1:length(col.sel)){
-          phiLambda[col.sel[j],,] = phiLambda2[j,,]
-        }
-        
-        dimension = 0
-        for (j in 1:p){
-          b = setdiff(1:m, sel.lambda[,j])
-          if (length(b) > 0){
-            phiLambda[j,b,] = 0.0
-          }
-          dimension = dimension + sum(sel.lambda[,j]!=0)
-        }
-        
-        #on veut calculer la vraisemblance avec toutes nos estimations
-        densite = vector("double",n)
-        for (r in 1:k)
-        {
-          delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
-          densite = densite + piLambda[r] *
-            det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-        }
-        llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
-        list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
-      }
-    }
-    )
-    return(out)
-  }
+		for (j in seq_along(col.sel))
+			phiLambda[col.sel[j],,] = phiLambda2[j,,]
+
+		dimension = 0
+		for (j in 1:p)
+		{
+			b = setdiff(1:m, sel.lambda[,j])
+			if (length(b) > 0)
+				phiLambda[j,b,] = 0.0
+			dimension = dimension + sum(sel.lambda[,j]!=0)
+		}
+
+		# on veut calculer la vraisemblance avec toutes nos estimations
+		densite = vector("double",n)
+		for (r in 1:k)
+		{
+			delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+			densite = densite + piLambda[r] *
+				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+		}
+		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
+		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
+	}
+
+	#Pour chaque lambda de la grille, on calcule les coefficients
+  out =
+		if (ncores > 1)
+			parLapply(cl, seq_along(glambda), computeAtLambda)
+		else
+			lapply(seq_along(glambda), computeAtLambda)
+
+	if (ncores > 1)
+    parallel::stopCluster(cl)
+
+	out
 }