X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=e8013a2dbc76cc11cb401f2259a5fa924309865c;hp=67fc1fcb99b377aeed743550fe8ca263b07663fc;hb=08f4604c778da8af7e26b52b1d433a6be82c3139;hpb=0eb161e3f3d018bce7d98fc85622d14910f89d43

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 67fc1fc..e8013a2 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,23 +8,23 @@
 #'
 #' export
 constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-	gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
+	gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
 {
-  if (ncores > 1)
+	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") )
+		cl = parallel::makeCluster(ncores)
+		parallel::clusterExport( cl, envir=environment(),
+			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
+			"tau","S","ncores","verbose") )
 	}
 
 	# Individual model computation
 	computeAtLambda <- function(lambda)
 	{
 		if (ncores > 1)
-			require("valse") #// nodes start with an ampty environment
+			require("valse") #nodes start with an empty environment
 
-    if (verbose)
+		if (verbose)
 			print(paste("Computations for lambda=",lambda))
 
 		n = dim(X)[1]
@@ -32,7 +32,7 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 		m = dim(phiInit)[2]
 		k = dim(phiInit)[3]
 
-		sel.lambda = selected[[lambda]]
+		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
 
@@ -44,43 +44,36 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 			X[,col.sel],Y,tau)
 		
 		# Eval dimension from the result + selected
-		phiLambda2 = res_EM$phi
-		rhoLambda = res_EM$rho
-		piLambda = res_EM$pi
-    phiLambda = array(0, dim = c(p,m,k))
+		phiLambda2 = res$phi
+		rhoLambda = res$rho
+		piLambda = res$pi
+		phiLambda = array(0, dim = c(p,m,k))
 		for (j in seq_along(col.sel))
 			phiLambda[col.sel[j],,] = phiLambda2[j,,]
+		dimension = length(unlist(sel.lambda))
 
-		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
+		# Computation of the loglikelihood
 		densite = vector("double",n)
 		for (r in 1:k)
 		{
-			delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+			delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
+			print(max(delta))
 			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 )
+		llhLambda = c( sum(artefact^2 * 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 =
+	# For each lambda, computation of the parameters
+	out =
 		if (ncores > 1)
-			parLapply(cl, glambda, computeAtLambda)
-		else
-			lapply(glambda, computeAtLambda)
+			parLapply(cl, 1:length(S), computeAtLambda)
+	else
+		lapply(1:length(S), computeAtLambda)
 
 	if (ncores > 1)
-    parallel::stopCluster(cl)
+		parallel::stopCluster(cl)
 
 	out
 }