EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau) { # Matrix dimensions n = dim(X)[1] p = dim(phiInit)[1] m = dim(phiInit)[2] k = dim(phiInit)[3] # Outputs phi = phiInit rho = rhoInit pi = piInit llh = -Inf S = array(0, dim=c(p,m,k)) # Algorithm variables gam = gamInit Gram2 = array(0, dim=c(p,p,k)) ps2 = array(0, dim=c(p,m,k)) b = rep(0, k) X2 = array(0, dim=c(n,p,k)) Y2 = array(0, dim=c(n,m,k)) EPS = 1e-15 for (ite in 1:maxi) { # Remember last pi,rho,phi values for exit condition in the end of loop Phi = phi Rho = rho Pi = pi # Calcul associé à Y et X for (r in 1:k) { for (mm in 1:m) Y2[,mm,r] = sqrt(gam[,r]) * Y[,mm] for (i in 1:n) X2[i,,r] = sqrt(gam[i,r]) * X[i,] for (mm in 1:m) ps2[,mm,r] = crossprod(X2[,,r],Y2[,mm,r]) for (j in 1:p) { for (s in 1:p) Gram2[j,s,r] = crossprod(X2[,j,r], X2[,s,r]) } } ########## #Etape M # ########## # Pour pi b = sapply( 1:k, function(r) sum(abs(phi[,,r])) ) gam2 = colSums(gam) a = sum(gam %*% log(pi)) # Tant que les props sont negatives kk = 0 pi2AllPositive = FALSE while (!pi2AllPositive) { pi2 = pi + 0.1^kk * ((1/n)*gam2 - pi) pi2AllPositive = all(pi2 >= 0) kk = kk+1 } # t(m) la plus grande valeur dans la grille O.1^k tel que ce soit décroissante ou constante while( kk < 1000 && -a/n + lambda * sum(pi^gamma * b) < -sum(gam2 * log(pi2))/n + lambda * sum(pi2^gamma * b) ) { pi2 = pi + 0.1^kk * (1/n*gam2 - pi) kk = kk + 1 } t = 0.1^kk pi = (pi + t*(pi2-pi)) / sum(pi + t*(pi2-pi)) #Pour phi et rho for (r in 1:k) { for (mm in 1:m) { ps = 0 for (i in 1:n) ps = ps + Y2[i,mm,r] * sum(X2[i,,r] * phi[,mm,r]) nY2 = sum(Y2[,mm,r]^2) rho[mm,mm,r] = (ps+sqrt(ps^2+4*nY2*gam2[r])) / (2*nY2) } } for (r in 1:k) { for (j in 1:p) { for (mm in 1:m) { S[j,mm,r] = -rho[mm,mm,r]*ps2[j,mm,r] + sum(phi[-j,mm,r] * Gram2[j,-j,r]) if (abs(S[j,mm,r]) <= n*lambda*(pi[r]^gamma)) phi[j,mm,r]=0 else if(S[j,mm,r] > n*lambda*(pi[r]^gamma)) phi[j,mm,r] = (n*lambda*(pi[r]^gamma)-S[j,mm,r]) / Gram2[j,j,r] else phi[j,mm,r] = -(n*lambda*(pi[r]^gamma)+S[j,mm,r]) / Gram2[j,j,r] } } } ########## #Etape E # ########## sumLogLLH2 = 0 for (i in 1:n) { # Update gam[,] sumLLH1 = 0 sumGamI = 0 for (r in 1:k) { gam[i,r] = pi[r] * exp(-0.5*sum( (Y[i,]%*%rho[,,r]-X[i,]%*%phi[,,r])^2 )) * det(rho[,,r]) sumLLH1 = sumLLH1 + gam[i,r] / (2*base::pi)^(m/2) sumGamI = sumGamI + gam[i,r] } sumLogLLH2 = sumLogLLH2 + log(sumLLH1) if(sumGamI > EPS) #else: gam[i,] is already ~=0 gam[i,] = gam[i,] / sumGamI } sumPen = sum(pi^gamma * b) last_llh = llh llh = -sumLogLLH2/n + lambda*sumPen dist = ifelse( ite == 1, llh, (llh-last_llh) / (1+abs(llh)) ) Dist1 = max( (abs(phi-Phi)) / (1+abs(phi)) ) Dist2 = max( (abs(rho-Rho)) / (1+abs(rho)) ) Dist3 = max( (abs(pi-Pi)) / (1+abs(Pi)) ) dist2 = max(Dist1,Dist2,Dist3) if (ite>=mini && (dist>= tau || dist2 >= sqrt(tau))) break } affec = apply(gam, 1, which.max) list( "phi"=phi, "rho"=rho, "pi"=pi, "llh"=llh, "S"=S, "affec"=affec ) }