n = nrow(Y)
m = ncol(Y)
p = ncol(X)
-
- Zinit1 = array(0, dim=c(n,20))
- betaInit1 = array(0, dim=c(p,m,k,20))
- sigmaInit1 = array(0, dim = c(m,m,k,20))
- phiInit1 = array(0, dim = c(p,m,k,20))
- rhoInit1 = array(0, dim = c(m,m,k,20))
+ 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(0,20,k)
- gamInit1 = array(0, dim=c(n,k,20))
+ 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 1:20)
+ for(repet in 1:nIte)
{
- distance_clus = dist(X)
+ distance_clus = dist(cbind(X,Y))
tree_hier = hclust(distance_clus)
Zinit1[,repet] = cutree(tree_hier, k)
miniInit = 10
maxiInit = 11
- new_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
- gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, tau=1e-4, fast)
- LLFEessai = new_EMG$LLF
+ init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+ gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
+ LLFEessai = init_EMG$LLF
LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}
-
- b = which.max(LLFinit1)
+ b = which.min(LLFinit1)
phiInit = phiInit1[,,,b]
rhoInit = rhoInit1[,,,b]
piInit = piInit1[b,]