-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
- X,Y,seuil,tau,selected)
+#' constructionModelesLassoMLE
+#'
+#' TODO: description
+#'
+#' @param ...
+#'
+#' @return ...
+#'
+#' export
+constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+ gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, fast=TRUE, verbose=FALSE)
{
- #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
- cl = parallel::makeCluster( parallel::detectCores() / 4 )
- parallel::clusterExport(cl=cl,
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(lambdaindex)
+ 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]
- #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
- #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
- res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
+ sel.lambda = S[[lambda]]$selected
+# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+ col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
- #comment évaluer la dimension à partir du résultat et de [not]selected ?
- #dimension = ...
+ if (length(col.sel) == 0)
+ return (NULL)
- #on veut calculer la vraisemblance avec toutes nos estimations
+ # lambda == 0 because we compute the EMV: no penalization here
+ res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+ X[,col.sel], Y, tau, fast)
+
+ # Eval dimension from the result + selected
+ phiLambda2 = res$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],,] = phiLambda2[j,,]
+ dimension = length(unlist(sel.lambda))
+
+ # Computation of the loglikelihood
densite = vector("double",n)
for (r in 1:k)
{
- delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
- densite = densite + pi[r] *
- det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+ delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
+ print(max(delta))
+ densite = densite + piLambda[r] *
+ det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
}
- llh = c( sum(log(densite[,lambdaIndex])), (dimension+m+1)*k-1 )
- list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
- })
- parallel::stopCluster(cl)
+ 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
}