X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=CCC.R;fp=CCC.R;h=9a17c08be58f56ddad1f5a4f11e9f48735a5725a;hb=08f4604c778da8af7e26b52b1d433a6be82c3139;hp=0000000000000000000000000000000000000000;hpb=086cf723817b690dc368d2f11b7b9e88d183e804;p=valse.git

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+#' 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 (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))
+		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, glambda, computeAtLambda)
+		else
+			lapply(glambda, computeAtLambda)
+
+	if (ncores > 1)
+		parallel::stopCluster(cl)
+
+	out
+}