n = nrow(Y)
m = ncol(Y)
p = ncol(X)
-
+
+ Zinit1 = array(0, dim=c(n,20)) #doute sur la taille
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))
+ Gam = matrix(0, n, k)
piInit1 = matrix(0,20,k)
gamInit1 = array(0, dim=c(n,k,20))
LLFinit1 = list()
require(mclust) # K-means with selection of K
for(repet in 1:20)
{
- clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+ clusters = Mclust(X,k) #default distance : euclidean #Mclust(matrix(c(X,Y)),k)
Zinit1[,repet] = clusters$classification
for(r in 1:k)
{
Z = Zinit1[,repet]
Z_bin = vec_bin(Z,r)
- Z_vec = Z_bin$Z #vecteur 0 et 1 aux endroits o? 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
- betaInit1[,,r,repet] =
- ginv(t(x[Z_indice,])%*%x[Z_indice,])%*%t(x[Z_indice,])%*%y[Z_indice,]
+ betaInit1[,,r,repet] = ginv(t(X[Z_indice,])%*%X[Z_indice,])%*%t(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
}
{
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 = 3 #(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet]) %*% (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,])
+ sumGamI = sum(Gam[i,])
gamInit1[i,,repet]= Gam[i,] / sumGamI
}
maxiInit = 11
new_EMG = .Call("EMGLLF",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)]
}