- n = nrow(Y)
- m = ncol(Y)
- p = ncol(X)
- nIte = 20
- Zinit1 = array(0, dim=c(n,nIte))
- betaInit1 = array(0, dim=c(p,m,k,nIte))
- sigmaInit1 = array(0, dim = c(m,m,k,nIte))
- phiInit1 = array(0, dim = c(p,m,k,nIte))
- rhoInit1 = array(0, dim = c(m,m,k,nIte))
- Gam = matrix(0, n, k)
- piInit1 = matrix(0,nIte,k)
- gamInit1 = array(0, dim=c(n,k,nIte))
- LLFinit1 = list()
-
- #require(MASS) #Moore-Penrose generalized inverse of matrix
- for(repet in 1:nIte)
- {
- distance_clus = dist(cbind(X,Y))
- tree_hier = hclust(distance_clus)
- Zinit1[,repet] = cutree(tree_hier, k)
-
- for(r in 1:k)
- {
- Z = Zinit1[,repet]
- Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
- if (length(Z_indice) == 1) {
- betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
- crossprod(t(X[Z_indice,]), Y[Z_indice,])
- } else {
- betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
- crossprod(X[Z_indice,], Y[Z_indice,])
- }
- sigmaInit1[,,r,repet] = diag(m)
- phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet]
- rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
- piInit1[repet,r] = mean(Z == r)
- }
-
- for(i in 1:n)
- {
- for(r in 1:k)
- {
- dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
- Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
- }
- sumGamI = sum(Gam[i,])
- gamInit1[i,,repet]= Gam[i,] / sumGamI
- }
-
- miniInit = 10
- maxiInit = 11
-
- init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
- gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
- LLFEessai = init_EMG$LLF
- LLFinit1[repet] = LLFEessai[length(LLFEessai)]
- }
- b = which.min(LLFinit1)
- phiInit = phiInit1[,,,b]
- rhoInit = rhoInit1[,,,b]
- piInit = piInit1[b,]
- gamInit = gamInit1[,,b]
-
- return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+ n <- nrow(Y)
+ m <- ncol(Y)
+ p <- ncol(X)
+ nIte <- 20
+ Zinit1 <- array(0, dim = c(n, nIte))
+ betaInit1 <- array(0, dim = c(p, m, k, nIte))
+ sigmaInit1 <- array(0, dim = c(m, m, k, nIte))
+ phiInit1 <- array(0, dim = c(p, m, k, nIte))
+ rhoInit1 <- array(0, dim = c(m, m, k, nIte))
+ Gam <- matrix(0, n, k)
+ piInit1 <- matrix(0, nIte, k)
+ gamInit1 <- array(0, dim = c(n, k, nIte))
+ LLFinit1 <- list()
+
+ # require(MASS) #Moore-Penrose generalized inverse of matrix
+ for (repet in 1:nIte)
+ {
+ distance_clus <- dist(cbind(X, Y))
+ tree_hier <- hclust(distance_clus)
+ Zinit1[, repet] <- cutree(tree_hier, k)
+
+ for (r in 1:k)
+ {
+ Z <- Zinit1[, repet]
+ Z_indice <- seq_len(n)[Z == r] #renvoit les indices où Z==r
+ if (length(Z_indice) == 1)
+ {
+ betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*%
+ crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
+ } else
+ {
+ betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*%
+ crossprod(X[Z_indice, ], Y[Z_indice, ])
+ }
+ sigmaInit1[, , r, repet] <- diag(m)
+ phiInit1[, , r, repet] <- betaInit1[, , r, repet] #/ sigmaInit1[,,r,repet]
+ rhoInit1[, , r, repet] <- solve(sigmaInit1[, , r, repet])
+ piInit1[repet, r] <- mean(Z == r)
+ }
+
+ for (i in 1:n)
+ {
+ for (r in 1:k)
+ {
+ dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet] - X[i,
+ ] %*% phiInit1[, , r, repet])
+ Gam[i, r] <- piInit1[repet, r] * det(rhoInit1[, , r, repet]) * exp(-0.5 *
+ dotProduct)
+ }
+ sumGamI <- sum(Gam[i, ])
+ gamInit1[i, , repet] <- Gam[i, ]/sumGamI
+ }
+
+ miniInit <- 10
+ maxiInit <- 11
+
+ init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet,
+ ], gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X,
+ Y, eps = 1e-04, fast)
+ LLFEessai <- init_EMG$LLF
+ LLFinit1[repet] <- LLFEessai[length(LLFEessai)]
+ }
+ b <- which.min(LLFinit1)
+ phiInit <- phiInit1[, , , b]
+ rhoInit <- rhoInit1[, , , b]
+ piInit <- piInit1[b, ]
+ gamInit <- gamInit1[, , b]
+
+ return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))