- 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()
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ 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 ou 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, ])
+ # TODO: next line is a division by zero if dotProduct is big
+ gamInit1[i, , repet] <- Gam[i, ]/sumGamI
+ }