R/initSmallEM.R

   1 vec_bin = function(X,r)
   2 {
   3     Z = c()
   4     indice = c()
   5     j = 1
   6     for (i in 1:length(X))
   7     {
   8         if(X[i] == r)
   9         {
  10             Z[i] = 1
  11             indice[j] = i
  12             j=j+1
  13         } else
  14             Z[i] = 0
  15     }
  16     return (list(Z=Z,indice=indice))
  17 }
  18
  19 initSmallEM = function(k,X,Y,tau)
  20 {
  21     n = nrow(Y)
  22     m = ncol(Y)
  23     p = ncol(X)
  24
  25     betaInit1 = array(0, dim=c(p,m,k,20))
  26     sigmaInit1 = array(0, dim = c(m,m,k,20))
  27     phiInit1 = array(0, dim = c(p,m,k,20))
  28     rhoInit1 = array(0, dim = c(m,m,k,20))
  29     piInit1 = matrix(0,20,k)
  30     gamInit1 = array(0, dim=c(n,k,20))
  31     LLFinit1 = list()
  32
  33     require(MASS) #Moore-Penrose generalized inverse of matrix
  34     for(repet in 1:20)
  35     {
  36         clusters = hclust(dist(y)) #default distance : euclidean
  37         #cutree retourne les indices (à quel cluster indiv_i appartient) d'un clustering hierarchique
  38         clusterCut = cutree(clusters,k)
  39         Zinit1[,repet] = clusterCut
  40
  41         for(r in 1:k)
  42         {
  43             Z = Zinit1[,repet]
  44             Z_bin = vec_bin(Z,r)
  45             Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits où Z==r
  46             Z_indice = Z_bin$indice #renvoit les indices où Z==r
  47
  48             betaInit1[,,r,repet] =
  49                 ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
  50             sigmaInit1[,,r,repet] = diag(m)
  51             phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
  52             rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
  53             piInit1[repet,r] = sum(Z_vec)/n
  54         }
  55
  56         for(i in 1:n)
  57         {
  58             for(r in 1:k)
  59             {
  60                 dotProduct = (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet]) %*%
  61                     (y[i,]%*%rhoInit1[,,r,repet]-x[i,]%*%phiInit1[,,r,repet])
  62                 Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
  63             }
  64             sumGamI = sum(gam[i,])
  65             gamInit1[i,,repet]= Gam[i,] / sumGamI
  66         }
  67
  68         miniInit = 10
  69         maxiInit = 11
  70
  71         new_EMG = .Call("EMGLLF",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
  72             gamInit1[,,repet],miniInit,maxiInit,1,0,x,y,tau)
  73         LLFEessai = new_EMG$LLF
  74         LLFinit1[repet] = LLFEessai[length(LLFEessai)]
  75     }
  76
  77     b = which.max(LLFinit1)
  78     phiInit = phiInit1[,,,b]
  79     rhoInit = rhoInit1[,,,b]
  80     piInit = piInit1[b,]
  81     gamInit = gamInit1[,,b]
  82
  83     return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
  84 }