X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=3967dfcf623892689d186b6ab7ee8979ddde35ed;hp=ed08b38c3ae873f528188f32cdb38c88dc0ef1a0;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=51485a7d0aafe7c31c9651fcc2e33ebd2f8a5e82

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
deleted file mode 100644
index ed08b38..0000000
--- a/pkg/R/constructionModelesLassoMLE.R
+++ /dev/null
@@ -1,88 +0,0 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
-                                       X,Y,seuil,tau,selected, parallel = FALSE)
-{
-  if (parallel) {
-    #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
-    cl = parallel::makeCluster( parallel::detectCores() / 4 )
-    parallel::clusterExport(cl=cl,
-                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","X","Y","seuil","tau"),
-                            envir=environment())
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    out = parLapply( seq_along(glambda), function(lambda)
-    {
-      n = dim(X)[1]
-      p = dim(phiInit)[1]
-      m = dim(phiInit)[2]
-      k = dim(phiInit)[3]
-      
-      #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
-      #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
-      res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
-      
-      #comment évaluer la dimension à partir du résultat et de [not]selected ?
-      #dimension = ...
-      
-      #on veut calculer la vraisemblance avec toutes nos estimations
-      densite = vector("double",n)
-      for (r in 1:k)
-      {
-        delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
-        densite = densite + pi[r] *
-          det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
-      list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
-    })
-    parallel::stopCluster(cl)
-    out
-  }
-  else {
-    #Pour chaque lambda de la grille, on calcule les coefficients
-    n = dim(X)[1]
-    p = dim(phiInit)[1]
-    m = dim(phiInit)[2]
-    k = dim(phiInit)[3]
-    L = length(selected)
-    phi = list()
-    phiLambda = array(0, dim = c(p,m,k))
-    rho = list()
-    pi = list()
-    llh = list()
-    
-    for (lambda in 1:L){
-      sel.lambda = selected[[lambda]]
-      col.sel = which(colSums(sel.lambda)!=0)
-      res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
-      phiLambda2 = res_EM$phi
-      rhoLambda = res_EM$rho
-      piLambda = res_EM$pi
-      for (j in 1:length(col.sel)){
-        phiLambda[col.sel[j],,] = phiLambda2[j,,]
-      }
-      
-      dimension = 0
-      for (j in 1:p){
-        b = setdiff(1:m, sel.lambda[,j])
-        if (length(b) > 0){
-          phiLambda[j,b,] = 0.0
-        }
-        dimension = dimension + sum(sel.lambda[,j]!=0)
-      }
-      
-      #on veut calculer la vraisemblance avec toutes nos estimations
-      densite = vector("double",n)
-      for (r in 1:k)
-      {
-        delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
-        densite = densite + piLambda[r] *
-          det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
-      rho[[lambda]] = rhoLambda
-      phi[[lambda]] = phiLambda
-      pi[[lambda]] = piLambda
-      llh[[lambda]] = llhLambda
-    }
-  }
-  return(list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))
-}