- # lambda == 0 because we compute the EMV: no penalization here
- res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
- X[,col.sel], Y, tau, fast)
-
- # Eval dimension from the result + selected
- phiLambda2 = res$phi
- rhoLambda = res$rho
- piLambda = res$pi
- phiLambda = array(0, dim = c(p,m,k))
- for (j in seq_along(col.sel))
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
- dimension = length(unlist(sel.lambda))
+ ## Affectations
+ Gam <- matrix(0, ncol = length(piLambda), nrow = n)
+ for (i in 1:n)
+ {
+ for (r in 1:length(piLambda))
+ {
+ sqNorm2 <- sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+ Gam[i, r] <- piLambda[r] * exp(-0.5 * sqNorm2) * det(rhoLambda[, , r])
+ }
+ }
+ Gam2 <- Gam/rowSums(Gam)
+ affec <- apply(Gam2, 1, which.max)
+ proba <- Gam2
+ LLH <- c(sum(log(apply(Gam,1,sum))), (dimension + m + 1) * k - 1)
+ # ## Computation of the loglikelihood
+ # # Precompute det(rhoLambda[,,r]) for r in 1...k
+ # detRho <- sapply(1:k, function(r) gdet(rhoLambda[, , r]))
+ # sumLogLLH <- 0
+ # for (i in 1:n)
+ # {
+ # # Update gam[,]; use log to avoid numerical problems
+ # logGam <- sapply(1:k, function(r) {
+ # log(piLambda[r]) + log(detRho[r]) - 0.5 *
+ # sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+ # })
+ #
+ # #logGam <- logGam - max(logGam) #adjust without changing proportions -> change the LLH
+ # gam <- exp(logGam)
+ # norm_fact <- sum(gam)
+ # sumLogLLH <- sumLogLLH + log(norm_fact) - m/2* log(2 * base::pi)
+ # }
+ #llhLambda <- c(-sumLogLLH/n, (dimension + m + 1) * k - 1)
+ list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = LLH, affec = affec, proba = proba)
+ }