correction emgllf.R
[valse.git] / src / test / generate_test_data / helpers / EMGrank.R
CommitLineData
c2028869
BG
1EMGLLF = function(Pi, Rho, mini, maxi, X, Y, tau, rank){
2 #matrix dimensions
3 n = dim(X)[1]
4 p = dim(X)[2]
5 m = dim(Rho)[2]
6 k = dim(Rho)[3]
7
8 #init outputs
9 phi = array(0, dim=c(p,m,k))
10 Z = rep(1, n)
11 Pi = piInit
12 LLF = 0
13
14 #local variables
15 Phi = array(0, dim=c(p,m,k))
16 deltaPhi = c(0)
17 sumDeltaPhi = 0
18 deltaPhiBufferSize = 20
19
20 #main loop
21 ite = 1
22 while(ite<=mini || (ite<=maxi && sumDeltaPhi>tau)){
23 #M step: Mise à jour de Beta (et donc phi)
24 for(r in 1:k){
25 Z_bin = vec_bin(Z,r)
26 Z_vec = Z_bin$vec #vecteur 0 et 1 aux endroits o? Z==r
27 Z_indice = Z_bin$indice
28 if(sum(Z_indice) == 0){
29 next
30 }
31 #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
32 [U,S,V] = svd(ginv(crossprod(X[Z_indice,]))%*% (X[Z_indice,])%*%Y[Z_indice,] )
33 #Set m-rank(r) singular values to zero, and recompose
34 #best rank(r) approximation of the initial product
35 S[rank(r)+1:end,] = 0
36 phi[,,r] = U %*%S%*%t(V)%*%Rho[,,r]
37 }
38
39 #Etape E et calcul de LLF
40 sumLogLLF2 = 0
41 for(i in 1:n){
42 sumLLF1 = 0
43 maxLogGamIR = -Inf
44 for(r in 1:k){
45 dotProduct = tcrossprod(Y[i,]%*%Rho[,,r]-X[i,]%*%phi[,,r])
46 logGamIR = log(Pi[r]) + log(det(Rho[,,r])) - 0.5*dotProduct
47 #Z[i] = index of max (gam[i,])
48 if(logGamIR > maxLogGamIR){
49 Z[i] = r
50 maxLogGamIR = logGamIR
51 }
52 sumLLF1 = sumLLF1 + exp(logGamIR) / (2*pi)^(m/2)
53 }
54 sumLogLLF2 = sumLogLLF2 + log(sumLLF1)
55 }
56
57 LLF = -1/n * sumLogLLF2
58
59 #update distance parameter to check algorithm convergence (delta(phi, Phi))
60 deltaPhi = c(deltaPhi, max(max(max((abs(phi-Phi))/(1+abs(phi))))) )
61 if(length(deltaPhi) > deltaPhiBufferSize){
62 deltaPhi = deltaPhi[2:length(deltaPhi)]
63 }
64 sumDeltaPhi = sum(abs(deltaPhi))
65
66 #update other local variables
67 Phi = phi
68 ite = ite+1
69
70 }
71 return(list(phi=phi, LLF=LLF))
72}