#' selectVariables
#'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' For a given lambda, construct the sets of relevant variables for each cluster.
#'
#' @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 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 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 X matrix of regressors
+#' @param Y matrix of responses
+#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
+#' @param eps threshold to say that EM algorithm has converged
+#' @param ncores Number or cores for parallel execution (1 to disable)
+#' @param fast boolean to enable or not the C function call
#'
#' @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 //
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
+ glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
{
- 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())
- }
+ if (ncores > 1) {
+ cl <- parallel::makeCluster(ncores, outfile = "")
+ parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
+ "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
+ }
+
+ # Computation for a fixed lambda
+ computeCoefs <- function(lambda)
+ {
+ params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
+ X, Y, eps, fast)
- # Calcul pour un lambda
- computeCoefs <-function(lambda)
- {
- params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
+ p <- ncol(X)
+ m <- ncol(Y)
- 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
+ if (m>1) {
+ seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+ } else {
+ if (any(params$phi[j, , ] > thresh))
+ 1
+ else
+ numeric(0)
+ }
+ })
- #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
- #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$phi[j,,]) > thresh, 1, any ) ] ) ))
- })
+ list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
+ }
- list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
- }
+ # For each lambda in the grid, we compute the coefficients
+ out <-
+ if (ncores > 1) {
+ parLapply(cl, glambda, computeCoefs)
+ } else {
+ lapply(glambda, computeCoefs)
+ }
+ if (ncores > 1)
+ parallel::stopCluster(cl)
- # Pour chaque lambda de la grille, on calcule les coefficients
- out <-
- if (ncores > 1){
- parLapply(cl, seq_along(glambda, computeCoefs))}
- else lapply(seq_along(glambda), computeCoefs)
- if (ncores > 1){
- parallel::stopCluster(cl)}
- out
+ print(out) #DEBUG TRACE
+ # Suppress models which are computed twice
+ # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
+ selec <- lapply(out, function(model) model$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
}
projects / valse.git / blobdiff