-
- # List (index k) of lists (index lambda) of models
- models_list <-
- if (ncores_outer > 1)
- parLapply(cl, kmin:kmax, computeModels)
- else
- lapply(kmin:kmax, computeModels)
- if (ncores_outer > 1)
- parallel::stopCluster(cl)
-
- if (! requireNamespace("capushe", quietly=TRUE))
- {
- warning("'capushe' not available: returning all models")
- return (models_list)
- }
-
- # Get summary "tableauRecap" from models
- 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]))) ) )
- data.frame(model=paste(i,".",seq_along(models),sep=""),
- pen=sumPen/1000, complexity=sumPen, contrast=LLH)
- } ) )
-
- modSel = capushe::capushe(data, 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
-
- models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+
+ # List (index k) of lists (index lambda) of models
+ models_list <-
+ if (ncores_outer > 1)
+ parLapply(cl, kmin:kmax, computeModels)
+ else
+ lapply(kmin:kmax, computeModels)
+ if (ncores_outer > 1)
+ parallel::stopCluster(cl)
+
+ if (! requireNamespace("capushe", quietly=TRUE))
+ {
+ warning("'capushe' not available: returning all models")
+ return (models_list)
+ }
+
+ # Get summary "tableauRecap" from models
+ tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+ models <- models_list[[i]]
+ #For a collection of models (same k, several lambda):
+ 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/n, complexity=sumPen, contrast=-LLH)
+ } ) )
+
+ print(tableauRecap)
+ tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
+
+ return(tableauRecap)
+
+ # 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)