X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=e8013a2dbc76cc11cb401f2259a5fa924309865c;hp=a49529c4a305aa3573662577b3be5018ee73c75d;hb=08f4604c778da8af7e26b52b1d433a6be82c3139;hpb=086cf723817b690dc368d2f11b7b9e88d183e804

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index a49529c..e8013a2 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,82 +8,72 @@
 #'
 #' 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, artefact = 1e3, verbose=FALSE)
 {
-  if (ncores > 1)
-  {
-    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 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]]$selected
-    #		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-    col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-    
-    if (length(col.sel) == 0)
-    {return (NULL)} else {
-      
-      # lambda == 0 because we compute the EMV: no penalization here
-      res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
-                      X[,col.sel],Y,tau)
-      
-      # Eval dimension from the result + selected
-      phiLambda2 = res_EM$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]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ? 
-        ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
-        if (length(b) > 0)
-          phiLambda[j,b,] = 0.0
-        dimension = dimension + sum(sel.lambda[[j]]!=0)
-      }
-      
-      # 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))
-        densite = densite + piLambda[r] *
-          det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llhLambda = c( sum(artefact^2 * 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
+	if (ncores > 1)
+	{
+		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 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]]$selected
+#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
+
+		if (length(col.sel) == 0)
+			return (NULL)
+
+		# lambda == 0 because we compute the EMV: no penalization here
+		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+			X[,col.sel],Y,tau)
+		
+		# Eval dimension from the result + selected
+		phiLambda2 = res$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],,] = phiLambda2[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))
+			densite = densite + piLambda[r] *
+				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+		}
+		llhLambda = c( sum(artefact^2 * 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
 }