X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoRank.R;h=dc88f676f1ed8ca11e0ec5b91013e304226df309;hp=339ba60bf3ef42251cae09bf573bbe1115844c22;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=aa480ac1fef50618978307a4df2cf9da1e285abc

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
deleted file mode 100644
index 339ba60..0000000
--- a/pkg/R/constructionModelesLassoRank.R
+++ /dev/null
@@ -1,84 +0,0 @@
-#' constructionModelesLassoRank
-#'
-#' TODO: description
-#'
-#' @param ...
-#'
-#' @return ...
-#'
-#' export
-constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rangmin,
-	rangmax, ncores, fast=TRUE, verbose=FALSE)
-{
-  n = dim(X)[1]
-  p = dim(X)[2]
-  m = dim(rho)[2]
-  k = dim(rho)[3]
-  L = dim(A1)[2]
-
-	# On cherche les rangs possiblement intéressants
-  deltaRank = rangmax - rangmin + 1
-  Size = deltaRank^k
-  Rank = matrix(0, nrow=Size, ncol=k)
-  for (r in 1:k)
-	{
-		# On veut le tableau de toutes les combinaisons de rangs possibles
-		# Dans la première colonne : on répète (rangmax-rangmin)^(k-1) chaque chiffre :
-		#   ça remplit la colonne
-		# Dans la deuxieme : on répète (rangmax-rangmin)^(k-2) chaque chiffre,
-		#   et on fait ça (rangmax-rangmin)^2 fois
-		# ...
-		# Dans la dernière, on répète chaque chiffre une fois,
-		#   et on fait ça (rangmin-rangmax)^(k-1) fois.
-    Rank[,r] = rangmin + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r))
-  }
-
-  if (ncores > 1)
-	{
-    cl = parallel::makeCluster(ncores, outfile='')
-    parallel::clusterExport( cl, envir=environment(),
-			varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau",
-			"Rank","m","phi","ncores","verbose") )
-	}
-
-	computeAtLambda <- function(lambdaIndex)
-	{
-		if (ncores > 1)
-			require("valse") #workers start with an empty environment
-
-    # on ne garde que les colonnes actives
-    # 'active' sera l'ensemble des variables informatives
-    active = A1[,lambdaIndex]
-    active = active[-(active==0)]
-		phi = array(0, dim=c(p,m,k,Size))
-		llh = matrix(0, Size, 2) #log-likelihood
-    if (length(active) > 0)
-		{
-      for (j in 1:Size)
-			{
-        res = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi,
-					X[,active], Y, tau, Rank[j,], fast)
-        llh = rbind(llh,
-					c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) ) )
-        phi[active,,,] = rbind(phi[active,,,], res$phi)
-      }
-    }
-		list("llh"=llh, "phi"=phi)
-	}
-
-	#Pour chaque lambda de la grille, on calcule les coefficients
-  out =
-		if (ncores > 1)
-			parLapply(cl, seq_along(glambda), computeAtLambda)
-		else
-			lapply(seq_along(glambda), computeAtLambda)
-
-	if (ncores > 1)
-    parallel::stopCluster(cl)
-
-	# TODO: this is a bit ugly. Better use bigmemory and fill llh/phi in-place
-	# (but this also adds a dependency...)
-	llh <- do.call( rbind, lapply(out, function(model) model$llh) )
-	phi <- do.call( rbind, lapply(out, function(model) model$phi) )
-	list("llh"=llh, "phi"=phi)
-}