-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
- X,Y,seuil,tau,selected, parallel = FALSE)
+#' constructionModelesLassoMLE
+#'
+#' TODO: description
+#'
+#' @param ...
+#'
+#' @return ...
+#'
+#' export
+constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+ gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
{
- if (parallel) {
- #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","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(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)
-
- #comment évaluer la dimension à partir du résultat et de [not]selected ?
- #dimension = ...
-
- #on veut calculer la vraisemblance avec toutes nos estimations
- densite = vector("double",n)
- for (r in 1:k)
- {
- delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
- densite = densite + pi[r] *
- det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
- }
- llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
- list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
- })
- 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") )
}
- else {
- #Pour chaque lambda de la grille, on calcule les coefficients
+
+ # 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]
- L = length(selected)
- phi = list()
- phiLambda = array(0, dim = c(p,m,k))
- rho = list()
- pi = list()
- llh = list()
- out = lapply( seq_along(selected), function(lambda)
- {
- sel.lambda = selected[[lambda]]
- col.sel = which(colSums(sel.lambda)!=0)
- res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
+ sel.lambda = S[[lambda]]$selected
+ # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
+ col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
+
+ if (length(col.sel) == 0)
+ {return (NULL)} else {
+
+ # lambda == 0 because we compute the EMV: no penalization here
+ res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+ X[,col.sel],Y,tau)
+
+ # Eval dimension from the result + selected
phiLambda2 = res_EM$phi
rhoLambda = res_EM$rho
piLambda = res_EM$pi
- for (j in 1:length(col.sel)){
+ 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){
+ 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)
+ dimension = dimension + sum(sel.lambda[[j]]!=0)
}
- #on veut calculer la vraisemblance avec toutes nos estimations
+ # Computation of the loglikelihood
densite = vector("double",n)
for (r in 1:k)
{
- delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+ 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(log(densite)), (dimension+m+1)*k-1 )
+ llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
}
- )
- return(out)
}
+
+ # 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
}