prepare EMGLLF / EMGrank wrappers, simplify folder generateTestData

[valse.git] / R / gridLambda.R

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 87e9299..855b4a6 100644 (file)
--- 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)
+}