| 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] |