m = dim(phiInit)[2]
#selectedVariables: list where element j contains vector of selected variables in [1,m]
- selectedVariables = sapply(1:p, function(j) { ## je me suis permise de changer le type,
- ##une liste de liste ca devenait compliqué je trouve pour choper ce qui nous intéresse
+ selectedVariables = lapply(1:p, function(j) {
#from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
#and finally return the corresponding indices
- #seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
- c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ],
- rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))
+ seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
})
list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
# Pour chaque lambda de la grille, on calcule les coefficients
out <-
- if (ncores > 1){
- parLapply(cl, seq_along(glambda, computeCoefs))}
- else lapply(seq_along(glambda), computeCoefs)
- if (ncores > 1){
- parallel::stopCluster(cl)}
+ if (ncores > 1)
+ parLapply(cl, glambda, computeCoefs)
+ else lapply(glambda, computeCoefs)
+ if (ncores > 1)
+ parallel::stopCluster(cl)
+
+ # Suppression doublons
+ sha1_array <- lapply(out, digest::sha1)
+ out[ !duplicated(sha1_array) ]
+
out
}