X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgridLambda.R;h=855b4a6f8fa0ea5d9d0354eae97f0bf94b89689c;hp=87e92997a2bc1c4c2d99f0a0786737d98d30a9bb;hb=31463ab809c0195273ff2760606ea65361d721ab;hpb=493a35bfea6d1210c94ced8fbfe3e572f0389ea5

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 87e9299..855b4a6 100644
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,29 +1,31 @@
-gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau){
-  n = nrow(X)
-  p = dimension(phiInit)[1]
-  m = dimension(phiInit)[2]
-  k = dimension(phiInit)[3]
-  list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
-  #.C("EMGLLF", phiInit = phiInit, rhoInit = rhoInit, ...)
-  phi = list_EMG[[1]]
-  rho = list_EMG[[2]]
-  pi = list_EMG[[3]]
-  S = list_EMG[[5]]
-  
-  grid = array(0, dim=c(p,m,k))
-  for(i in 1:p){
-    for(j in 1:m){
-      grid[i,j,] = abs(S[i,j,]) / (n*pi^gamma)
-    }
-  }
-  grid = unique(grid)
-  grid = grid[grid <=1 ]
-  
-  return(grid)
-}
+#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
+#' @param phiInit value for phi
+#' @param rhoInt	value for rho
+#' @param piInit	value for pi
+#' @param gamInit value for gamma
+#' @param mini		minimum number of iterations in EM algorithm
+#' @param maxi		maximum number of iterations in EM algorithm
+#' @param tau		threshold to stop EM algorithm
+#' @return the grid of regularization parameters
+#' @export
+#-----------------------------------------------------------------------
+gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
+{
+	n = nrow(X)
+	p = dim(phiInit)[1]
+	m = dim(phiInit)[2]
+	k = dim(phiInit)[3]
+
+	list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
 
+	grid = array(0, dim=c(p,m,k))
+	for (i in 1:p)
+	{
+		for (j in 1:m)
+			grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
+	}
+	grid = unique(grid)
+	grid = grid[grid <=1]
 
-#test pour voir si formatage à la fin de grid ok
-grid= array(mvrnorm(5*5*2,1,1), dim=c(5,5,2))
-grid = unique(grid)
-grid = grid[grid<= 1 ]
+	return(grid)
+}