X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=238160cb6c52c506525eb57b676dd93efd6a4864;hp=634c27396507901537f72d3238c075c187677157;hb=4d9db27f0d1749e5577038dedbc5f4d0826f2772;hpb=43d76c49d2f98490abc782c7e8a8b94baee40247

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 634c273..238160c 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -123,36 +123,39 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
   
   print(tableauRecap)
   tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
-  modSel = capushe::capushe(tableauRecap, n)
-  indModSel <-
-    if (selecMod == 'DDSE')
-      as.numeric(modSel@DDSE@model)
-  else if (selecMod == 'Djump')
-    as.numeric(modSel@Djump@model)
-  else if (selecMod == 'BIC')
-    modSel@BIC_capushe$model
-  else if (selecMod == 'AIC')
-    modSel@AIC_capushe$model
   
-  mod = as.character(tableauRecap[indModSel,1])
-  listMod = as.integer(unlist(strsplit(mod, "[.]")))
-  modelSel = models_list[[listMod[1]]][[listMod[2]]]
+  return(tableauRecap)
   
-  ##Affectations
-  Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
-  for (i in 1:n){
-    for (r in 1:length(modelSel$pi)){
-      sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
-      Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
-    }
-  }
-  Gam = Gam/rowSums(Gam)
-  modelSel$affec = apply(Gam, 1,which.max)
-  modelSel$proba = Gam
-  
-  if (plot){
-    print(plot_valse(X,Y,modelSel,n))
-  }
-  
-  return(modelSel)
+  # modSel = capushe::capushe(tableauRecap, n)
+  # indModSel <-
+  #   if (selecMod == 'DDSE')
+  #     as.numeric(modSel@DDSE@model)
+  # else if (selecMod == 'Djump')
+  #   as.numeric(modSel@Djump@model)
+  # else if (selecMod == 'BIC')
+  #   modSel@BIC_capushe$model
+  # else if (selecMod == 'AIC')
+  #   modSel@AIC_capushe$model
+  # 
+  # mod = as.character(tableauRecap[indModSel,1])
+  # listMod = as.integer(unlist(strsplit(mod, "[.]")))
+  # modelSel = models_list[[listMod[1]]][[listMod[2]]]
+  # 
+  # ##Affectations
+  # Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
+  # for (i in 1:n){
+  #   for (r in 1:length(modelSel$pi)){
+  #     sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
+  #     Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
+  #   }
+  # }
+  # Gam = Gam/rowSums(Gam)
+  # modelSel$affec = apply(Gam, 1,which.max)
+  # modelSel$proba = Gam
+  # 
+  # if (plot){
+  #   print(plot_valse(X,Y,modelSel,n))
+  # }
+  # 
+  # return(modelSel)
 }