X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=3967dfcf623892689d186b6ab7ee8979ddde35ed;hp=ac543199f3a62264ff7e75a56ece3faed56f9aa3;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=5965d116de1595372c8d34281551183fd3799038

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
deleted file mode 100644
index ac54319..0000000
--- a/pkg/R/constructionModelesLassoMLE.R
+++ /dev/null
@@ -1,79 +0,0 @@
-#' constructionModelesLassoMLE
-#'
-#' Construct a collection of models with the Lasso-MLE procedure.
-#' 
-#'
-#' @param ...
-#'
-#' @return ...
-#'
-#' export
-constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-	gamma, X, Y, thresh, tau, 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","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]]$selected
-#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-		if (length(col.sel) == 0)
-			return (NULL)
-
-		# lambda == 0 because we compute the EMV: no penalization here
-		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
-			X[,col.sel], Y, tau, fast)
-		
-		# Eval dimension from the result + selected
-		phiLambda2 = res$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
-}