X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=99959cac1b83c789e03e84853a6e2018f8619f98;hp=4e9b374c26e950aa560d2faa12cf7eac2717c0c8;hb=3921ba9b5ea85bcc190245ac7da9ee9da1658b9f;hpb=b9b0b72a2c8f7f0d1a3216528aefcec0a92c6c99

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 4e9b374..99959ca 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,68 +1,79 @@
 #' 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=3)
+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, 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)
+  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())
+  }
 
-		p = dim(phiInit)[1]
-		m = dim(phiInit)[2]
+  # Computation for a fixed lambda
+  computeCoefs <- function(lambda)
+  {
+    params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
+      X, Y, eps, fast)
 
-		#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 ) ]
-		})
+    p <- ncol(X)
+    m <- ncol(Y)
 
-		list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
-	}
+    # 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)
+      }
+    })
 
-	# 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)
+    list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
+  }
 
-	# Suppression doublons
-	sha1_array <- lapply(out, digest::sha1)
-	out[ !duplicated(sha1_array) ]
+  # 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)
 
-	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
 }