- ##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)