fix the problem with the likelihood for the R code

[valse.git] / pkg / R / main.R

diff --git a/pkg/R/main.R b/pkg/R/main.R
index de473f7..695a23f 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -26,8 +26,8 @@
 #' #TODO: a few examples
 #' @export
 valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
-       eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
-       size_coll_mod=50, fast=TRUE, verbose=FALSE)
+       eps=1e-4, kmin=2, kmax=4, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
+       size_coll_mod=50, fast=TRUE, verbose=FALSE, plot = TRUE)
 {
   p = dim(X)[2]
   m = dim(Y)[2]
@@ -75,7 +75,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
       models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
-                               mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
+                               mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
     }
                else
                {
@@ -83,7 +83,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
                                print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
       #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
                                rank.min, rank.max, ncores_inner, fast, verbose)
     }
                #attention certains modeles sont NULL après selectVariables
@@ -110,17 +110,15 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
        tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
                models <- models_list[[i]]
                #Pour un groupe de modeles (même k, différents lambda):
-               LLH <- sapply( models, function(model) model$llh )
-               k == length(models[[1]]$pi)
-               # TODO: chuis pas sûr du tout des lignes suivantes...
-               #       J'ai l'impression qu'il manque des infos
-               sumPen = sapply( models, function(model)
-                       sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) )
+               LLH <- sapply( models, function(model) model$llh[1] )
+               k = length(models[[1]]$pi)
+               sumPen = sapply(models, function(model)
+                 k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
                data.frame(model=paste(i,".",seq_along(models),sep=""),
-                       pen=sumPen/1000, complexity=sumPen, contrast=LLH)
+                       pen=sumPen/n, complexity=sumPen, contrast=-LLH)
        } ) )
-
-  modSel = capushe::capushe(data, n)
+print(tableauRecap)
+  modSel = capushe::capushe(tableauRecap, n)
   indModSel <-
                if (selecMod == 'DDSE')
                        as.numeric(modSel@DDSE@model)
@@ -130,6 +128,12 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
                        modSel@BIC_capushe$model
                else if (selecMod == 'AIC')
                        modSel@AIC_capushe$model
-       
-  models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+
+  mod = as.character(tableauRecap[indModSel,1])
+  listMod = as.integer(unlist(strsplit(mod, "[.]")))
+  if (plot){
+    print(plot_valse(models_list[[listMod[1]]][[listMod[2]]],n))
+  }
+  models_list[[listMod[1]]][[listMod[2]]]
+  
 }