m = ncol(Y)
p = ncol(X)
- Zinit1 = array(0, dim=c(n,20)) #doute sur la taille
+ 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))
LLFinit1 = list()
require(MASS) #Moore-Penrose generalized inverse of matrix
- require(mclust) # K-means with selection of K
for(repet in 1:20)
{
- clusters = Mclust(X,k) #default distance : euclidean #Mclust(matrix(c(X,Y)),k)
- Zinit1[,repet] = clusters$classification
-
+ distance_clus = dist(X)
+ tree_hier = hclust(distance_clus)
+ Zinit1[,repet] = cutree(tree_hier, k)
+
for(r in 1:k)
{
Z = Zinit1[,repet]
- Z_bin = vec_bin(Z,r)
- Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
- Z_indice = Z_bin$indice #renvoit les indices o? Z==r
+ Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
- betaInit1[,,r,repet] = ginv( crossprod(X[Z_indice,]) ) %*% crossprod(X[Z_indice,], Y[Z_indice,])
+ betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+ crossprod(X[Z_indice,], Y[Z_indice,])
sigmaInit1[,,r,repet] = diag(m)
- phiInit1[,,r,repet] = betaInit1[,,r,repet]/sigmaInit1[,,r,repet]
+ phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet]
rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
- piInit1[repet,r] = sum(Z_vec)/n
+ piInit1[repet,r] = mean(Z == r)
}
for(i in 1:n)
{
for(r in 1:k)
{
- dotProduct = (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) %*% (Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
+ dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
}
sumGamI = sum(Gam[i,])
maxiInit = 11
new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
- gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
+ gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,tau)
LLFEessai = new_EMG$LLF
LLFinit1[repet] = LLFEessai[length(LLFEessai)]
}