R/essai16mars.R

   1 meanX = rep(0,6)
   2 covX = 0.1*diag(6)
   3
   4 covY = array(dim = c(5,5,2))
   5 covY[,,1] = 0.1*diag(5)
   6 covY[,,2] = 0.2*diag(5)
   7
   8 beta = array(dim = c(6,5,2))
   9 beta[,,2] = matrix(c(rep(2,12),rep(0, 18)))
  10 beta[,,1] = matrix(c(rep(1,12),rep(0, 18)))
  11
  12 n = 500
  13
  14 pi = c(0.4,0.6)
  15
  16 source('~/valse/R/generateSampleInputs.R')
  17 data = generateXY(meanX,covX,covY, pi, beta, n)
  18
  19 X = data$X
  20 Y = data$Y
  21
  22 k = 2
  23
  24 n = nrow(Y)
  25 m = ncol(Y)
  26 p = ncol(X)
  27
  28 Zinit1 = array(0, dim=c(n))
  29 betaInit1 = array(0, dim=c(p,m,k))
  30 sigmaInit1 = array(0, dim = c(m,m,k))
  31 phiInit1 = array(0, dim = c(p,m,k))
  32 rhoInit1 = array(0, dim = c(m,m,k))
  33 Gam = matrix(0, n, k)
  34 piInit1 = matrix(0,k)
  35 gamInit1 = array(0, dim=c(n,k))
  36 LLFinit1 = list()
  37
  38 require(MASS) #Moore-Penrose generalized inverse of matrix
  39
  40   distance_clus = dist(X)
  41   tree_hier = hclust(distance_clus)
  42   Zinit1 = cutree(tree_hier, k)
  43   sum(Zinit1==1)
  44
  45   for(r in 1:k)
  46   {
  47     Z = Zinit1
  48     Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
  49     if (length(Z_indice) == 1) {
  50       betaInit1[,,r] = ginv(crossprod(t(X[Z_indice,]))) %*%
  51         crossprod(t(X[Z_indice,]), Y[Z_indice,])
  52     } else {
  53       betaInit1[,,r] = ginv(crossprod(X[Z_indice,])) %*%
  54         crossprod(X[Z_indice,], Y[Z_indice,])
  55     }
  56     sigmaInit1[,,r] = diag(m)
  57     phiInit1[,,r] = betaInit1[,,r] #/ sigmaInit1[,,r]
  58     rhoInit1[,,r] = solve(sigmaInit1[,,r])
  59     piInit1[r] = mean(Z == r)
  60   }
  61
  62   for(i in 1:n)
  63   {
  64     for(r in 1:k)
  65     {
  66       dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r]-X[i,]%*%phiInit1[,,r])
  67       Gam[i,r] = piInit1[r]*det(rhoInit1[,,r])*exp(-0.5*dotProduct)
  68     }
  69     sumGamI = sum(Gam[i,])
  70     gamInit1[i,]= Gam[i,] / sumGamI
  71   }
  72
  73   miniInit = 10
  74   maxiInit = 101
  75
  76   new_EMG = EMGLLF(phiInit1,rhoInit1,piInit1,gamInit1,miniInit,maxiInit,1,0,X,Y,1e-6)
  77
  78   new_EMG$phi
  79   new_EMG$pi
  80   LLFEessai = new_EMG$LLF
  81   LLFinit1 = LLFEessai[length(LLFEessai)]
  82
  83
  84 b = which.max(LLFinit1)
  85 phiInit = phiInit1[,,,b]
  86 rhoInit = rhoInit1[,,,b]
  87 piInit = piInit1[b,]
  88 gamInit = gamInit1[,,b]