int main(void) {
mocapy_seed((uint)924573);
// Number of trainining sequences
int N = 2000;
// Allocate storage in each node.
int max_num_dat_points = N;
// Sequence lengths
int T = 1;
// Gibbs sampling parameters
int MCMC_BURN_IN = 10;
// HMM hidden and observed node sizes
uint H_SIZE=2;
uint O_SIZE=20;
bool init_random=true;
// The target DBN (This DBN generates the data)
Node* th0 = NodeFactory::new_discrete_node(H_SIZE, "th0", init_random);
Node* th1 = NodeFactory::new_discrete_node(H_SIZE, "th1", init_random);
Node* to0 = NodeFactory::new_GD_node("to0", O_SIZE , max_num_dat_points);
cout<< "Done making GD node" << endl;
DBN tdbn;
tdbn.set_slices(vec(th0, to0), vec(th1, to0));
tdbn.add_intra("th0", "to0");
tdbn.add_inter("th0", "th1");
cout<< "model has been defined" << endl;
tdbn.construct();
// The model DBN (this DBN will be trained)
// For mh0, get the CPD from th0 and fix parameters
Node* mh0 = NodeFactory::new_discrete_node(H_SIZE, "mh0", init_random, CPD(), th0, true );
Node* mh1 = NodeFactory::new_discrete_node(H_SIZE, "mh1", init_random);
Node* mo0 = NodeFactory::new_GD_node("mo0",O_SIZE, max_num_dat_points);
DBN mdbn;
mdbn.set_slices(vec(mh0, mo0), vec(mh1, mo0));
mdbn.add_intra("mh0", "mo0");
mdbn.add_inter("mh0", "mh1");
mdbn.construct();
cout << "*** TARGET ***" << endl;
cout << *th0 << endl;
cout << *th1 << endl;
cout << *to0 << endl;
cout << "*** MODEL ***" << endl;
cout << *mh0 << endl;
cout << *mh1 << endl;
cout << *mo0 << endl;
vector<Sequence> seq_list;
vector< MDArray<eMISMASK> > mismask_list;
cout << "Generating data" << endl;
MDArray<eMISMASK> mismask;
mismask.repeat(T, vec(MOCAPY_HIDDEN, MOCAPY_OBSERVED));
// Setting parameters
//CPD pars2;
CPD pars;
int t = 4;
pars.set_shape(H_SIZE,2*(O_SIZE-1));
//pars2.set_shape(H_SIZE,2*(O_SIZE-1));
vector<double> parvec;
for(uint i = 0;i < O_SIZE-1;i++){
parvec.push_back(t* 1.0);
}
for(double i = O_SIZE-1;i >= 1;i--){
parvec.push_back(t*i);
}
// pars.set_values(parvec);
double s = 2;
for(uint i = 0;i < O_SIZE-1;i++){
parvec.push_back(s);
}
for(double i = s*(O_SIZE-1);i >= s*1;i=i-s){
parvec.push_back(i);
}
pars.set_values(parvec);
((GDNode*)to0)->get_densities()->set_parameters(pars);
pair<Sequence, double> seq_ll;
// Generate the data
double sum_LL(0);
for (int i=0; i<N; i++) {
seq_ll = tdbn.sample_sequence(T);
sum_LL += seq_ll.second;
seq_list.push_back(seq_ll.first);
mismask_list.push_back(mismask);
}
cout << "Average LL: " << sum_LL/N << endl;
GibbsRandom mcmc = GibbsRandom(&mdbn);
EMEngine em = EMEngine(&mdbn, &mcmc, &seq_list, &mismask_list);
cout << "Starting EM loop" << endl;
double bestLL=-100000;
uint it_no_improvement(0);
uint i(0);
bool l;
// Start EM loop
while (it_no_improvement<100) {
em.do_E_step(1, MCMC_BURN_IN, true);
double ll = em.get_loglik();
cout << "LL= " << ll;
if (ll > bestLL) {
cout << " * saving model *" << endl;
cout << *mo0 << endl;
l = true;
//mdbn.save("discrete_hmm.dbn");
bestLL = ll;
it_no_improvement=0;
}
else {
it_no_improvement++; cout << endl;
}
i++;
em.do_M_step();
if (l){
l=false;
}
}
cout << "DONE" << endl;
// mdbn.load("discrete_hmm.dbn");
cout << "*** TARGET ***" << endl;
cout << *th0 << endl;
cout << *th1 << endl;
cout << *to0 << endl;
cout << "*** MODEL ***" << endl;
cout << *mh0 << endl;
cout << *mh1 << endl;
cout << *mo0 << endl;
delete th0;
delete th1;
delete to0;
delete mh0;
delete mh1;
delete mo0;
return EXIT_SUCCESS;
}
int main(void) {
mocapy_seed((uint)5556575);
// Number of trainining sequences
int N = 500;
// Sequence lengths
int T = 100;
// Gibbs sampling parameters
int MCMC_BURN_IN = 10;
// HMM hidden and observed node sizes
uint H_SIZE=2;
bool init_random=false;
CPD th0_cpd;
th0_cpd.set_shape(2); th0_cpd.set_values(vec(0.1, 0.9));
CPD th1_cpd;
th1_cpd.set_shape(2,2); th1_cpd.set_values( vec(0.95, 0.05, 0.1, 0.9));
// The target DBN (This DBN generates the data)
Node* th0 = NodeFactory::new_discrete_node(H_SIZE, "th0", init_random, th0_cpd);
Node* th1 = NodeFactory::new_discrete_node(H_SIZE, "th1", init_random, th1_cpd);
Node* to0 = NodeFactory::new_kent_node("to0");
DBN tdbn;
tdbn.set_slices(vec(th0, to0), vec(th1, to0));
tdbn.add_intra("th0", "to0");
tdbn.add_inter("th0", "th1");
tdbn.construct();
// The model DBN (this DBN will be trained)
// For mh0, get the CPD from th0 and fix parameters
Node* mh0 = NodeFactory::new_discrete_node(H_SIZE, "mh0", init_random, CPD(), th0, true );
Node* mh1 = NodeFactory::new_discrete_node(H_SIZE, "mh1", init_random);
Node* mo0 = NodeFactory::new_kent_node("mo0");
DBN mdbn;
mdbn.set_slices(vec(mh0, mo0), vec(mh1, mo0));
mdbn.add_intra("mh0", "mo0");
mdbn.add_inter("mh0", "mh1");
mdbn.construct();
cout << "*** TARGET ***" << endl;
cout << *th0 << endl;
cout << *th1 << endl;
cout << *to0 << endl;
cout << "*** MODEL ***" << endl;
cout << *mh0 << endl;
cout << *mh1 << endl;
cout << *mo0 << endl;
vector<Sequence> seq_list;
vector< MDArray<eMISMASK> > mismask_list;
cout << "Generating data" << endl;
MDArray<eMISMASK> mismask;
mismask.repeat(T, vec(MOCAPY_HIDDEN, MOCAPY_OBSERVED));
// Generate the data
double sum_LL(0);
for (int i=0; i<N; i++) {
pair<Sequence, double> seq_ll = tdbn.sample_sequence(T);
sum_LL += seq_ll.second;
seq_list.push_back(seq_ll.first);
mismask_list.push_back(mismask);
}
cout << "Average LL: " << sum_LL/N << endl;
GibbsRandom mcmc = GibbsRandom(&mdbn);
EMEngine em = EMEngine(&mdbn, &mcmc, &seq_list, &mismask_list);
cout << "Starting EM loop" << endl;
double bestLL=-1000;
uint it_no_improvement(0);
uint i(0);
// Start EM loop
while (it_no_improvement<10) {
em.do_E_step(1, MCMC_BURN_IN, true);
double ll = em.get_loglik();
cout << "LL= " << ll;
if (ll > bestLL) {
cout << " * saving model *" << endl;
mdbn.save("kent_hmm.dbn");
bestLL = ll;
it_no_improvement=0;
}
else { it_no_improvement++; cout << endl; }
i++;
em.do_M_step();
}
cout << "DONE" << endl;
mdbn.load("kent_hmm.dbn");
cout << "*** TARGET ***" << endl;
cout << *th0 << endl;
cout << *th1 << endl;
cout << *to0 << endl;
cout << "*** MODEL ***" << endl;
cout << *mh0 << endl;
cout << *mh1 << endl;
cout << *mo0 << endl;
delete th0;
delete th1;
delete to0;
delete mh0;
delete mh1;
delete mo0;
return EXIT_SUCCESS;
}