X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=b86498559d532c0fd6385781441d1c5db90bf944;hp=e8013a2dbc76cc11cb401f2259a5fa924309865c;hb=fb6e49cb85308c3f99cc98fe955aa7c36839c819;hpb=08f4604c778da8af7e26b52b1d433a6be82c3139

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index e8013a2..b864985 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,11 +8,11 @@
 #'
 #' export
 constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-	gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
+	gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE)
 {
 	if (ncores > 1)
 	{
-		cl = parallel::makeCluster(ncores)
+		cl = parallel::makeCluster(ncores, outfile='')
 		parallel::clusterExport( cl, envir=environment(),
 			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
 			"tau","S","ncores","verbose") )
@@ -31,17 +31,15 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 		p = dim(phiInit)[1]
 		m = dim(phiInit)[2]
 		k = dim(phiInit)[3]
-
 		sel.lambda = S[[lambda]]$selected
 #		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)
+			X[,col.sel], Y, tau, fast)
 		
 		# Eval dimension from the result + selected
 		phiLambda2 = res$phi
@@ -49,19 +47,20 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 		piLambda = res$pi
 		phiLambda = array(0, dim = c(p,m,k))
 		for (j in seq_along(col.sel))
-			phiLambda[col.sel[j],,] = phiLambda2[j,,]
+			phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],]
 		dimension = length(unlist(sel.lambda))
 
 		# Computation of the loglikelihood
 		densite = vector("double",n)
 		for (r in 1:k)
 		{
-			delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
-			print(max(delta))
+		  if (length(col.sel)==1){
+		    delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r])))
+		  } else 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)
+				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
 		}
-		llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
+		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
 		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
 	}
 
@@ -69,8 +68,8 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 	out =
 		if (ncores > 1)
 			parLapply(cl, 1:length(S), computeAtLambda)
-	else
-		lapply(1:length(S), computeAtLambda)
+		else
+			lapply(1:length(S), computeAtLambda)
 
 	if (ncores > 1)
 		parallel::stopCluster(cl)