double HmmFilter::initialize(const Data * dp){
uint S = state_space_size();
pi = markov_->pi0();
if(dp->missing()) logp = 0;
else for(uint s=0; s<S; ++s) logp[s] = models_[s]->pdf(dp, true);
pi = log(pi) + logp;
double m = max(pi);
pi = exp(pi-m);
double nc = sum(pi);
double loglike = m + log(nc);
pi/=nc;
return loglike;
}
//------------------------------------------------------------
void HmmEmFilter::bkwd_smoothing(const std::vector<Ptr<Data> > & dv){
// pi was set by fwd;
uint n = dv.size();
uint S = state_space_size();
for(uint i=n-1; i!=0; --i){
for(uint s=0; s<S; ++s) models_[s]->add_mixture_data(dv[i], pi[s]);
markov_->suf()->add_transition_distribution(P[i]);
bkwd_1(pi, P[i], logp, one);
}
pi = P[1] * one;
for(uint s=0; s<S; ++s) models_[s]->add_mixture_data(dv[0], pi[s]);
markov_->suf()->add_initial_distribution(pi);
}
double MarkovModel::loglike(const Vector &serialized_params)const{
const Vec &icount(suf()->init());
const Mat &tcount(suf()->trans());
int S = state_space_size();
TransitionProbabilityMatrix transition_probabilities(S);
Vec logpi0(log(pi0()));
Mat logQ(log(Q()));
double ans= icount.dot(logpi0);
ans+= el_mult_sum(tcount, logQ);
return ans;
}
double HmmFilter::fwd(const std::vector<Ptr<Data> > &dv){
logQ = log(markov_->Q() );
uint n = dv.size();
uint S = state_space_size();
if(logp.size()!=S) logp.resize(S);
if(P.size() < n) P.resize(n);
double loglike = initialize(dv[0].get());
for(uint i=1; i<n; ++i){
if(dv[i]->missing()) logp = 0;
else for(uint s=0; s<S; ++s) logp[s] = models_[s]->pdf(dv[i].get(), true);
loglike += fwd_1(pi, P[i], logQ, logp, one);
}
return loglike;
}
void MarkovModel::fix_pi0_uniform(){
uint S = state_space_size();
set_pi0(Vec(S, 1.0/S));
pi0_status=Uniform; }
void MarkovSuf::resize(uint p){
if(state_space_size()!=p){
trans_ = Mat(p,p, 0.0);
init_ = Vec(p, 0.0);}}