X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=cdc0ec00f672d534d65c8a9b5dfb951b79a5e468;hp=65fbde546b2c298cd4fccca285a91a8655534cfe;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=fb6e49cb85308c3f99cc98fe955aa7c36839c819

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
deleted file mode 100644
index 65fbde5..0000000
--- a/pkg/R/selectVariables.R
+++ /dev/null
@@ -1,75 +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
-#' @param ncores Number or cores for parallel execution (1 to disable)
-#'
-#' @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=3, fast=TRUE)
-{
-  if (ncores > 1)
-  {
-    cl = parallel::makeCluster(ncores, outfile='')
-    parallel::clusterExport(cl=cl,
-                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
-                            envir=environment())
-  }
-  
-  # Computation for a fixed lambda
-  computeCoefs <- function(lambda)
-  {
-    params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau,fast)
-    
-    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$pi)
-  }
-  
-  # For each lambda in the grid, we compute the coefficients
-  out <-
-    if (ncores > 1)
-      parLapply(cl, glambda, computeCoefs)
-  else
-    lapply(glambda, computeCoefs)
-  if (ncores > 1)
-    parallel::stopCluster(cl)
-  # Suppress models which are computed twice
-  #En fait, ca ca fait la comparaison de tous les parametres
-  #On veut juste supprimer ceux qui ont les memes variables sélectionnées
-  #sha1_array <- lapply(out, digest::sha1)
-  #out[ duplicated(sha1_array) ]
-  selec = lapply(out, function(model) model$selected)
-  ind_dup = duplicated(selec)
-  ind_uniq = which(!ind_dup)
-  out2 = list()
-  for (l in 1:length(ind_uniq)){
-    out2[[l]] = out[[ind_uniq[l]]]
-  }
-  out2
-}