X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=760da40be6952bad6312c36636d99df410009583;hp=ba6f125cb16d94c6de8bdc65dd855578398b12c5;hb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed;hpb=20d12623f4f395ba126570b3230fc80214191d8e

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index ba6f125..760da40 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,4 +1,4 @@
-#' constructionModelesLassoMLE
+#' constructionModelesLassoMLE 
 #'
 #' Construct a collection of models with the Lasso-MLE procedure.
 #' 
@@ -20,72 +20,72 @@
 #' @return a list with several models, defined by phi, rho, pi, llh
 #'
 #' @export
-constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y,
-	 eps, S, ncores=3, fast=TRUE, verbose=FALSE)
-{
-	if (ncores > 1)
-	{
-		cl = parallel::makeCluster(ncores, outfile='')
-		parallel::clusterExport( cl, envir=environment(),
-			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps",
-			"S","ncores","fast","verbose") )
-	}
-
-	# Individual model computation
-	computeAtLambda <- function(lambda)
-	{
-		if (ncores > 1)
-			require("valse") #nodes start with an empty 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 = S[[lambda]]$selected
-#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-		if (length(col.sel) == 0)
-			return (NULL)
-
-		# lambda == 0 because we compute the EMV: no penalization here
-		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
-			X[,col.sel], Y, eps, fast)
-		
-		# Eval dimension from the result + selected
-		phiLambda2 = res$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],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)
-		{
-		  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(-diag(tcrossprod(delta))/2.0)
-		}
-		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
-		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
-	}
-
-	# For each lambda, computation of the parameters
-	out =
-		if (ncores > 1)
-			parLapply(cl, 1:length(S), computeAtLambda)
-		else
-			lapply(1:length(S), computeAtLambda)
-
-	if (ncores > 1)
-		parallel::stopCluster(cl)
-
-	out
+constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
+  maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE)
+  {
+  if (ncores > 1)
+  {
+    cl <- parallel::makeCluster(ncores, outfile = "")
+    parallel::clusterExport(cl, envir = environment(), varlist = c("phiInit", 
+      "rhoInit", "gamInit", "mini", "maxi", "gamma", "X", "Y", "eps", "S", 
+      "ncores", "fast", "verbose"))
+  }
+  
+  # Individual model computation
+  computeAtLambda <- function(lambda)
+  {
+    if (ncores > 1) 
+      require("valse")  #nodes start with an empty 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 <- S[[lambda]]$selected
+    # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+    col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
+    if (length(col.sel) == 0) 
+      return(NULL)
+    
+    # lambda == 0 because we compute the EMV: no penalization here
+    res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi, 
+      gamma, 0, X[, col.sel], Y, eps, fast)
+    
+    # Eval dimension from the result + selected
+    phiLambda2 <- res$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], 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)
+    {
+      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(-diag(tcrossprod(delta))/2)
+    }
+    llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
+    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
+  }
+  
+  # For each lambda, computation of the parameters
+  out <- if (ncores > 1) 
+    parLapply(cl, 1:length(S), computeAtLambda) else lapply(1:length(S), computeAtLambda)
+  
+  if (ncores > 1) 
+    parallel::stopCluster(cl)
+  
+  out
 }