pkg/R/constructionModelesLassoMLE_essai.R

   1 constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,selected){
   2   #get matrix sizes
   3   n = dim(X)[1]
   4   p = dim(phiInit)[1]
   5   m = dim(phiInit)[2]
   6   k = dim(phiInit)[3]
   7   L = length(glambda)
   8
   9   #output parameters
  10   phi = array(0, dim = c(p,m,k,L))
  11   rho = array(dim = c(m,m,k,L))
  12   pi = array( dim = c(k,L))
  13   lvraisemblance = array( dim = c(L,2))
  14
  15   for (lambdaIndex in 1:L){
  16     # Procedure Lasso-MLE
  17     a = selected[,1,lambdaIndex]
  18     a(a==0) = c()
  19     if (length(a) != 0){
  20       res_EM = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,X[,a],Y,tau)
  21       phiLambda = res_EM$phi
  22       rhoLambda = res_EM$rho
  23       piLambda = res_EM$pi
  24       for (j in 1:length(a)){
  25         phi[a[j],,,lambdaIndex] = phiLambda[j,,]
  26       }
  27       rho[,,,lambdaIndex] = rhoLambda
  28       pi[,lambdaIndex] = piLambda
  29
  30       dimension = 0
  31       for (j in 1:p){
  32         b = A2[j,2:end,lambdaIndex]
  33         b[b==0] = c()
  34         if (length(b) > 0){
  35         phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0.0
  36         }
  37         c = A1[j,2:end,lambdaIndex]
  38         c[c==0] = c()
  39         dimension = dimension + length(c)
  40       }
  41
  42       #on veut calculer l'EMV avec toutes nos estimations
  43       densite = array(n,L)
  44       for (i in 1:n){
  45         for (r in 1:k){
  46           delta = Y[i,]*rho[,,r,lambdaIndex] - X[i,a]*phi[a,,r,lambdaIndex]
  47           densite[i,lambdaIndex] = densite[i,lambdaIndex] + pi[r,lambdaIndex]*det(rho[,,r,lambdaIndex])/(sqrt(2*base::pi))^m*exp(-delta %*% delta/2.0)
  48         }
  49       }
  50
  51       lvraisemblance(lambdaIndex,1) = sum(log(densite[,lambdaIndex]))
  52       lvraisemblance(lambdaIndex,2) = (dimension+m+1)*k-1
  53     }
  54   }
  55 }