X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=a49529c4a305aa3573662577b3be5018ee73c75d;hp=67fc1fcb99b377aeed743550fe8ca263b07663fc;hb=086cf723817b690dc368d2f11b7b9e88d183e804;hpb=567a7c388285ef17ce1e49d295527937dbfadf66

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 67fc1fc..a49529c 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -8,79 +8,82 @@
 #'
 #' 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)
-	{
+  {
     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
-
+                             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 = 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$pi
-    phiLambda = array(0, dim = c(p,m,k))
-		for (j in seq_along(col.sel))
-			phiLambda[col.sel[j],,] = phiLambda2[j,,]
-
-		dimension = 0
-		for (j in 1:p)
-		{
-			b = setdiff(1:m, sel.lambda[,j])
-			if (length(b) > 0)
-				phiLambda[j,b,] = 0.0
-			dimension = dimension + sum(sel.lambda[,j]!=0)
-		}
-
-		# on veut calculer la vraisemblance avec toutes nos estimations
-		densite = vector("double",n)
-		for (r in 1:k)
-		{
-			delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
-			densite = densite + piLambda[r] *
-				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-		}
-		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
-		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
-	}
-
-	#Pour chaque lambda de la grille, on calcule les coefficients
+      print(paste("Computations for lambda=",lambda))
+    
+    n = dim(X)[1]
+    p = dim(phiInit)[1]
+    m = dim(phiInit)[2]
+    k = dim(phiInit)[3]
+    
+    sel.lambda = S[[lambda]]$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, glambda, computeAtLambda)
-		else
-			lapply(glambda, computeAtLambda)
-
-	if (ncores > 1)
+    if (ncores > 1)
+      parLapply(cl, 1:length(S), computeAtLambda)
+  else
+    lapply(1:length(S), computeAtLambda)
+  
+  if (ncores > 1)
     parallel::stopCluster(cl)
-
-	out
+  
+  out
 }