X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fvalse.R;h=46a5fd045680bbc8f6e9246222706cda184210cd;hp=445ea263641256863777258e682c356de2f43a8e;hb=51485a7d0aafe7c31c9651fcc2e33ebd2f8a5e82;hpb=77fde6fc68ce70ca07a371be4511993d5516085d

diff --git a/pkg/R/valse.R b/pkg/R/valse.R
index 445ea26..46a5fd0 100644
--- a/pkg/R/valse.R
+++ b/pkg/R/valse.R
@@ -15,8 +15,8 @@
 #' @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 = 'BIC',gamma = 1,mini = 10,
+                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,
                  rang.min = 1,rang.max = 10) {
   ##################################
   #core workflow: compute all models
@@ -24,12 +24,14 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
   
   p = dim(phiInit)[1]
   m = dim(phiInit)[2]
+  n = dim(X)[1]
   
+  tableauRecap = array(, 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 +41,18 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
     rhoInit <<- init$rhoInit
     piInit	<<- init$piInit
     gamInit <<- init$gamInit
-    
+    source('~/valse/pkg/R/gridLambda.R')
     gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
     
     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,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+    params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+    ## etrange : params et params 2 sont différents ...
+    
     selected <<- params$selected
     Rho <<- params$Rho
     Pi <<- params$Pi
@@ -55,36 +61,9 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
       print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(
-        phiInit, rhoInit,piInit,tauInit,mini,maxi,
-        gamma,gridLambda,X,Y,thresh,eps,selected)
-      ################################################
-      ### Regarder la SUITE
-      r1 = runProcedure1()
-      Phi2 = Phi
-      Rho2 = Rho
-      Pi2 = Pi
-      
-      if (is.null(dim(Phi2)))
-        #test was: size(Phi2) == 0
-      {
-        Phi[, , 1:k] <<- r1$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 = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
+      D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]
     } else {
       print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
@@ -106,12 +85,18 @@ 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')
+  
+  tableauRecap = array(dim = c())
   if (selecMod == 'SlopeHeuristic') {
     
   } else if (selecMod == 'BIC') {
-    
+    BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
+    indMinBIC = which.min(BIC)
+    return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])
   } else if (selecMod == 'AIC') {
     
   }