-EMGLLF = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
+require(MASS)
+EMGrank = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
#matrix dimensions
n = dim(X)[1]
p = dim(X)[2]
while(ite<=mini || (ite<=maxi && sumDeltaPhi>tau)){
#M step: Mise à jour de Beta (et donc phi)
for(r in 1:k){
- Z_bin = vec_bin(Z,r)
+ Z_bin = valse:::vec_bin(Z,r)
Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
Z_indice = Z_bin$indice
if(sum(Z_indice) == 0){
next
}
#U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
- [U,S,V] = svd(ginv(crossprod(X[Z_indice,]))%*% (X[Z_indice,])%*%Y[Z_indice,] )
+ sv = svd(ginv( crossprod(X[Z_indice,]) ) %*% crossprod(X[Z_indice,], Y[Z_indice,]) )
+ S = diag(sv$d)
+ U = sv$u
+ V = sv$v
#Set m-rank(r) singular values to zero, and recompose
#best rank(r) approximation of the initial product
- S[rank(r)+1:end,] = 0
- phi[,,r] = U %*%S%*%t(V)%*%Rho[,,r]
+ if(r==k){
+ j_r_1 = length(S)
+ }
+ else{
+ j_r_1 = c(rank[r]+1:length(S))
+ }
+ S[j_r_1] = 0
+ S = diag(S, nrow = ncol(U))
+ phi[,,r] = U %*% S %*% t(V) %*% Rho[,,r]
}
#Etape E et calcul de LLF
#update distance parameter to check algorithm convergence (delta(phi, Phi))
deltaPhi = c(deltaPhi, max(max(max((abs(phi-Phi))/(1+abs(phi))))) )
if(length(deltaPhi) > deltaPhiBufferSize){
- deltaPhi = deltaPhi[2:length(deltaPhi)]
+ l_1 = c(2:length(deltaPhi))
+ deltaPhi = deltaPhi[l_1]
}
sumDeltaPhi = sum(abs(deltaPhi))
}
return(list(phi=phi, LLF=LLF))
-}
\ No newline at end of file
+}