essai fusion

[valse.git] / pkg / R / selectVariables.R

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
deleted file mode 100644 (file)
index b4fc0ab..0000000
--- a/pkg/R/selectVariables.R
+++ /dev/null
@@ -1,66 +0,0 @@
-#' selectVariables
-#'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
-#'
-#' @param phiInit an initial estimator for phi (size: p*m*k)
-#' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit       an initial estimator for pi (size : k)
-#' @param gamInit an initial estimator for gamma
-#' @param mini         minimum number of iterations in EM algorithm
-#' @param maxi         maximum number of iterations in EM algorithm
-#' @param gamma         power in the penalty
-#' @param glambda grid of regularization parameters
-#' @param X                     matrix of regressors
-#' @param Y                     matrix of responses
-#' @param thres         threshold to consider a coefficient to be equal to 0
-#' @param tau           threshold to say that EM algorithm has converged
-#'
-#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
-#'
-#' @examples TODO
-#'
-#' @export
-#'
-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
-       X,Y,thresh,tau, ncores=1) #ncores==1 ==> no //
-{
-       if (ncores > 1)
-       {
-               cl = parallel::makeCluster(ncores)
-               parallel::clusterExport(cl=cl,
-                       varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
-                       envir=environment())
-       }
-
-       # Calcul pour un lambda
-       computeCoefs <-function(lambda)
-       {
-               params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
-
-               p = dim(phiInit)[1]
-               m = dim(phiInit)[2]
-
-               #selectedVariables: list where element j contains vector of selected variables in [1,m]
-               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$phi[j,,]) > thresh, 1, any ) ]
-               })
-
-               list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
-       }
-
-       # Pour chaque lambda de la grille, on calcule les coefficients
-       out <-
-               if (ncores > 1)
-                       parLapply(cl, 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
-}