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fix/improve selectVariables.R
author
Benjamin Auder
<benjamin.auder@somewhere>
Sun, 2 Apr 2017 12:28:17 +0000
(14:28 +0200)
committer
Benjamin Auder
<benjamin.auder@somewhere>
Sun, 2 Apr 2017 12:28:17 +0000
(14:28 +0200)
pkg/R/selectVariables.R
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diff --git
a/pkg/R/selectVariables.R
b/pkg/R/selectVariables.R
index
e4ed179
..
b4fc0ab
100644
(file)
--- a/
pkg/R/selectVariables.R
+++ b/
pkg/R/selectVariables.R
@@
-41,13
+41,10
@@
selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambd
m = dim(phiInit)[2]
#selectedVariables: list where element j contains vector of selected variables in [1,m]
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
#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)
})
list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
@@
-55,10
+52,15
@@
selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambd
# Pour chaque lambda de la grille, on calcule les coefficients
out <-
# 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
}
out
}