double LikelihoodInfEngineHMM::calc_ll(std::vector<Sequence *> &seqs, std::vector<MDArray<eMISMASK> > &mismasks, bool multiply_by_input) {
double ll = 0.0;
for (uint i=0; i<seqs.size(); i++) {
ll += calc_ll(*seqs[i], mismasks[i], 0, -1, multiply_by_input);
}
return ll;
}
static int reml(VEC *Y, MAT *X, MAT **Vk, int n_k, int max_iter,
double fit_limit, VEC *teta) {
volatile int n_iter = 0;
int i;
volatile double rel_step = DBL_MAX;
VEC *rhs = VNULL;
VEC *dteta = VNULL;
MAT *Vw = MNULL, *Tr_m = MNULL, *VinvIminAw = MNULL;
Vw = m_resize(Vw, X->m, X->m);
VinvIminAw = m_resize(VinvIminAw, X->m, X->m);
rhs = v_resize(rhs, n_k);
Tr_m = m_resize(Tr_m, n_k, n_k);
dteta = v_resize(dteta, n_k);
while (n_iter < max_iter && rel_step > fit_limit) {
print_progress(n_iter, max_iter);
n_iter++;
dteta = v_copy(teta, dteta);
/* fill Vw, calc VinvIminAw, rhs; */
for (i = 0, m_zero(Vw); i < n_k; i++)
ms_mltadd(Vw, Vk[i], teta->ve[i], Vw); /* Vw = Sum_i teta[i]*V[i] */
VinvIminAw = calc_VinvIminAw(Vw, X, VinvIminAw, n_iter == 1);
calc_rhs_Tr_m(n_k, Vk, VinvIminAw, Y, rhs, Tr_m);
/* Tr_m * teta = Rhs; symmetric, solve for teta: */
LDLfactor(Tr_m);
LDLsolve(Tr_m, rhs, teta);
if (DEBUG_VGMFIT) {
printlog("teta_%d [", n_iter);
for (i = 0; i < teta->dim; i++)
printlog(" %g", teta->ve[i]);
printlog("] -(log.likelyhood): %g\n",
calc_ll(Vw, X, Y, n_k));
}
v_sub(teta, dteta, dteta); /* dteta = teta_prev - teta_curr */
if (v_norm2(teta) == 0.0)
rel_step = 0.0;
else
rel_step = v_norm2(dteta) / v_norm2(teta);
} /* while (n_iter < gl_iter && rel_step > fit_limit) */
print_progress(max_iter, max_iter);
if (n_iter == gl_iter)
pr_warning("No convergence after %d iterations", n_iter);
if (DEBUG_VGMFIT) { /* calculate and report covariance matrix */
/* first, update to current est */
for (i = 0, m_zero(Vw); i < n_k; i++)
ms_mltadd(Vw, Vk[i], teta->ve[i], Vw); /* Vw = Sum_i teta[i]*V[i] */
VinvIminAw = calc_VinvIminAw(Vw, X, VinvIminAw, 0);
calc_rhs_Tr_m(n_k, Vk, VinvIminAw, Y, rhs, Tr_m);
m_inverse(Tr_m, Tr_m);
sm_mlt(2.0, Tr_m, Tr_m); /* Var(YAY)=2tr(AVAV) */
printlog("Lower bound of parameter covariance matrix:\n");
m_logoutput(Tr_m);
printlog("# Negative log-likelyhood: %g\n", calc_ll(Vw, X, Y, n_k));
}
m_free(Vw);
m_free(VinvIminAw);
m_free(Tr_m);
v_free(rhs);
v_free(dteta);
return (n_iter < max_iter && rel_step < fit_limit); /* converged? */
}
