X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fvalse.R;h=d5d10ced034dd9d43b7e39794926864423bb6528;hp=445ea263641256863777258e682c356de2f43a8e;hb=2652fb8797f3097361ba23c7f3790be9754db987;hpb=f33f35efc9a01f93bb61959522d90ee6a76b892e

diff --git a/pkg/R/valse.R b/pkg/R/valse.R
index 445ea26..d5d10ce 100644
--- a/pkg/R/valse.R
+++ b/pkg/R/valse.R
@@ -3,7 +3,7 @@
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
 #' @param procedure among 'LassoMLE' or 'LassoRank'
-#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'
+#' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
 #' @param gamma integer for the power in the penaly, by default = 1
 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
@@ -15,21 +15,24 @@
 #' @return a list with estimators of parameters
 #' @export
 #-----------------------------------------------------------------------
-valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
-                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
+                 maxi = 50,eps = 1e-4,kmin = 2,kmax = 2,
                  rang.min = 1,rang.max = 10) {
   ##################################
   #core workflow: compute all models
   ##################################
   
-  p = dim(phiInit)[1]
-  m = dim(phiInit)[2]
+  p = dim(X)[2]
+  m = dim(Y)[2]
+  n = dim(X)[1]
   
+  model = list()
+  tableauRecap = array(0, dim=c(1000,4))
+  cpt = 0
   print("main loop: over all k and all lambda")
-  for (k in kmin:kmax)
-  {
+  
+  for (k in kmin:kmax){
     print(k)
-    
     print("Parameters initialization")
     #smallEM initializes parameters by k-means and regression model in each component,
     #doing this 20 times, and keeping the values maximizing the likelihood after 10
@@ -39,14 +42,19 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
     rhoInit <<- init$rhoInit
     piInit	<<- init$piInit
     gamInit <<- init$gamInit
+    grid_lambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
     
-    gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
-    
+    if (length(grid_lambda)>100){
+      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
+    }
     print("Compute relevant parameters")
     #select variables according to each regularization parameter
     #from the grid: A1 corresponding to selected variables, and
     #A2 corresponding to unselected variables.
-    params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda,X,Y,1e-8,eps)
+    
+    params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda,X,Y,1e-8,eps)
+    #params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda[seq(1,length(grid_lambda), by=3)],X,Y,1e-8,eps)
+    ## etrange : params et params 2 sont différents ...
     selected <<- params$selected
     Rho <<- params$Rho
     Pi <<- params$Pi
@@ -55,36 +63,13 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
       print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(
-        phiInit, rhoInit,piInit,tauInit,mini,maxi,
-        gamma,gridLambda,X,Y,thresh,eps,selected)
-      ################################################
-      ### Regarder la SUITE
-      r1 = runProcedure1()
-      Phi2 = Phi
-      Rho2 = Rho
-      Pi2 = Pi
-      
-      if (is.null(dim(Phi2)))
-        #test was: size(Phi2) == 0
-      {
-        Phi[, , 1:k] <<- r1$phi
-        Rho[, , 1:k] <<- r1$rho
-        Pi[1:k,] <<- r1$pi
-      } else
-      {
-        Phi <<-
-          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
-        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
-        Rho <<-
-          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
-        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
-        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
-        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
-        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
-        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
+      model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
+      llh = matrix(ncol = 2)
+      for (l in seq_along(model[[k]])){
+        llh = rbind(llh, model[[k]][[l]]$llh)
       }
+      LLH = llh[-1,1]
+      D = llh[-1,2]
     } else {
       print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
@@ -106,13 +91,23 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
         Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
       }
     }
+    tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
+    cpt = cpt+length(model[[k]])
   }
   print('Model selection')
-  if (selecMod == 'SlopeHeuristic') {
-    
+  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
+  tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
+  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
+  require(capushe)
+  modSel = capushe(data, n)
+  if (selecMod == 'DDSE') {
+    indModSel = as.numeric(modSel@DDSE@model)
+  } else if (selecMod == 'Djump') {
+    indModSel = as.numeric(modSel@Djump@model)
   } else if (selecMod == 'BIC') {
-    
+    indModSel = modSel@BIC_capushe$model
   } else if (selecMod == 'AIC') {
-    
+    indModSel = modSel@AIC_capushe$model
   }
+  return(model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]])
 }