X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=cdc0ec00f672d534d65c8a9b5dfb951b79a5e468;hp=b23eac21b36a4de70ff592e367f2ccb302e8ded5;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=aa480ac1fef50618978307a4df2cf9da1e285abc

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
deleted file mode 100644
index b23eac2..0000000
--- a/pkg/R/selectVariables.R
+++ /dev/null
@@ -1,68 +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())
-	}
-
-	# Calcul pour un 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)
-	}
-
-	# 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
-}