X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FconstructionModelesLassoMLE_essai.R;fp=R%2FconstructionModelesLassoMLE_essai.R;h=0000000000000000000000000000000000000000;hp=e295d0cb5ea763740804309035973fc0c940fc34;hb=d36b8e8accd1bdab7dbabea6b66d5e5f01068af6;hpb=f33f35efc9a01f93bb61959522d90ee6a76b892e

diff --git a/R/constructionModelesLassoMLE_essai.R b/R/constructionModelesLassoMLE_essai.R
deleted file mode 100644
index e295d0c..0000000
--- a/R/constructionModelesLassoMLE_essai.R
+++ /dev/null
@@ -1,55 +0,0 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,selected){
-  #get matrix sizes
-  n = dim(X)[1]
-  p = dim(phiInit)[1]
-  m = dim(phiInit)[2]
-  k = dim(phiInit)[3]
-  L = length(glambda)
-  
-  #output parameters
-  phi = array(0, dim = c(p,m,k,L))
-  rho = array(dim = c(m,m,k,L))
-  pi = array( dim = c(k,L))
-  lvraisemblance = array( dim = c(L,2))
-  
-  for (lambdaIndex in 1:L){
-    # Procedure Lasso-MLE  
-    a = selected[,1,lambdaIndex]
-    a(a==0) = c()
-    if (length(a) != 0){
-      res_EM = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,X[,a],Y,tau)
-      phiLambda = res_EM$phi
-      rhoLambda = res_EM$rho
-      piLambda = res_EM$pi
-      for (j in 1:length(a)){
-        phi[a[j],,,lambdaIndex] = phiLambda[j,,]
-      }
-      rho[,,,lambdaIndex] = rhoLambda
-      pi[,lambdaIndex] = piLambda
-      
-      dimension = 0
-      for (j in 1:p){
-        b = A2[j,2:end,lambdaIndex]
-        b[b==0] = c()
-        if (length(b) > 0){
-        phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0.0
-        }
-        c = A1[j,2:end,lambdaIndex]
-        c[c==0] = c()
-        dimension = dimension + length(c)
-      }
-      
-      #on veut calculer l'EMV avec toutes nos estimations
-      densite = array(n,L)
-      for (i in 1:n){
-        for (r in 1:k){
-          delta = Y[i,]*rho[,,r,lambdaIndex] - X[i,a]*phi[a,,r,lambdaIndex]
-          densite[i,lambdaIndex] = densite[i,lambdaIndex] + pi[r,lambdaIndex]*det(rho[,,r,lambdaIndex])/(sqrt(2*base::pi))^m*exp(-delta %*% delta/2.0)
-        }
-      }
-      
-      lvraisemblance(lambdaIndex,1) = sum(log(densite[,lambdaIndex]))
-      lvraisemblance(lambdaIndex,2) = (dimension+m+1)*k-1
-    }
-  }
-}
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