void TestHSMM::test_loglik(const char* filename, size_t ID, string type){
HSMMparam param(filename);
vector<Real> likelihood_test;
FactorGraph *graph;
JTree *jt;
// Set some constants
size_t maxiter = 10000;
Real tol = 1e-9;
size_t verb = 0;
// Store the constants in a PropertySet object
PropertySet opts;
opts.set("maxiter",maxiter); // Maximum number of iterations
opts.set("tol",tol); // Tolerance for convergence
opts.set("verbose",verb); // Verbosity (amount of output generated)
cout << "Now we do testing...\n";
for(size_t i=0; i<test_data.size(); i++) {
//initialize HSMM of size equal the number of observations
graph = new FactorGraph();
graph->createHSMMFactorGraph(param.init, param.dist, test_data[i].size());
jt = new JTree(*graph, opts("updates",string("HUGIN"))("heuristic",string("MINWEIGHT")) );
//clamp the observation variables to their observed values
for(size_t j = 0; j < test_data[i].size(); j++ ){
//cout << "clamping var" << test_data[i][j].first << " to value " << test_data[i][j].second << "\n";
jt->clamp(test_data[i][j].first, test_data[i][j].second);
}
jt->init();
jt->run();
//compute normalized loglikelyhood
likelihood_test.push_back(jt->logZ()/test_data[i].size());
delete jt;
delete graph;
cout << "Tested point " << i << " out of " << test_data.size() <<"\n";
}
cout << "done.\n";
ofstream os;
stringstream result;
result << string("data/HSMMlikelihood_") << type << string("_") << ID << string(".txt");
os.open(result.str().c_str(), ios::trunc);
for(size_t i=0; i<likelihood_test.size(); i++){
os << likelihood_test.at(i)<<"\n";
}
}
void TestHSMM::test_loglik(const char* filename, size_t ID, string type, int dummy, int ID2){
//"type" specifies the suffix of the output file "test" or "true"
//read in HSMM parameters from textfile
HSMMparam param(filename, 0);
vector<Real> likelihood_test;
FactorGraph *graph;
JTree *jt;
VarSet X;
// Set some constants
size_t maxiter = 10000;
Real tol = 1e-9;
size_t verb = 0;
// Store the constants in a PropertySet object
PropertySet opts;
opts.set("maxiter",maxiter); // Maximum number of iterations
opts.set("tol",tol); // Tolerance for convergence
opts.set("verbose",verb); // Verbosity (amount of output generated)
cout << "Now we do testing...\n";
for(size_t i=0; i<test_data.size(); i++) {
//initialize HSMM of size equal the number of observations
graph = new FactorGraph();
graph->createHSMMFactorGraph(param.init, param.dist, test_data[i].size(), 0);
jt = new JTree(*graph, opts("updates",string("HUGIN"))("heuristic",string("MINWEIGHT")) );
//clamp the observation variables to their observed values
for(size_t j = 0; j < test_data[i].size(); j++ ){
//cout << "clamping var" << test_data[i][j].first << " to value " << test_data[i][j].second << "\n";
jt->clamp(test_data[i][j].first, test_data[i][j].second);
}
jt->init();
jt->run();
//compute normalized loglikelihood
//likelihood_test.push_back(jt->logZ()/test_data[i].size());
likelihood_test.push_back(jt->logZ());
delete jt;
delete graph;
cout << "Tested point " << i << " out of " << test_data.size() <<"\n";
//cout << "jt->logZ() = " << likelihood_test.at(0) << "\n";
//exit(1);
}
cout << "done.\n";
ofstream os;
stringstream result;
//ID2 is used to wirte test results with fixed number of training iterations
if(ID2 >= 0){
result << string("data/HSMMlikelihood_") << type << string("_") << ID << string("-") << ID2 << string(".txt");
}
else{
result << string("data/HSMMlikelihood_") << type << string("_") << ID << string(".txt");
}
os.open(result.str().c_str(), ios::trunc);
os.unsetf ( std::ios::floatfield );
os.precision(18);
for(size_t i=0; i<likelihood_test.size(); i++){
os << likelihood_test.at(i)<<"\n";
}
}
void TestHSMM::test_marginal_cut(const char* filename, size_t ID){
HSMMparam param(filename);
FactorGraph *graph;
JTree *jt;
// Set some constants
size_t maxiter = 10000;
Real tol = 1e-9;
size_t verb = 0;
//window size
size_t W = 20;
size_t start = 0;
// Store the constants in a PropertySet object
PropertySet opts;
opts.set("maxiter",maxiter); // Maximum number of iterations
opts.set("tol",tol); // Tolerance for convergence
opts.set("verbose",verb); // Verbosity (amount of output generated)
Factor O_last;
vector< vector<Real> > all_marginal;
vector<Real> sequence_marginal;
all_marginal.reserve(test_data.size());
cout << "Now we do testing...\n";
for(size_t i=0; i<test_data.size(); i++) {
//allocate memory
sequence_marginal.reserve(test_data[i].size());
//initialize HSMM of ever increasing size up to test_data[i].size()
graph = new FactorGraph();
graph->createHSMMFactorGraph(param.init, param.dist, test_data[i].size());
jt = new JTree(*graph, opts("updates",string("HUGIN"))("heuristic",string("MINWEIGHT")) );
jt->init();
jt->run();
O_last = jt->calcMarginal(graph->var(4));
sequence_marginal.push_back( log(O_last.p().get(test_data[i][0].second)) );
delete jt;
for(size_t k=1; k < test_data[i].size(); k++){
jt = new JTree(*graph, opts("updates",string("HUGIN"))("heuristic",string("MINWEIGHT")) );
//clamp a window of observable variables to their values, except last variable which is not clamped
start = k-W;
if(start < 0) start = 0;
for(size_t j = start; j <= k-1; j++){
jt->clamp(test_data[i][j].first, test_data[i][j].second);
}
jt->init();
jt->run();
//compute p(o_last=c | o_1...o_{last-1})
//this will give us a distribution: {o_last=1, o_last=2, ... o_last=M}
O_last = jt->calcMarginal(graph->var(3*k+4));
//since we have a specific observation at last time step: o_last=c, get its probability:
sequence_marginal.push_back( log(O_last.p().get(test_data[i][k].second)) );
delete jt;
}
cout << "Tested point " << i << " out of " << test_data.size() <<"\n";
all_marginal.push_back(sequence_marginal);
sequence_marginal.clear();
delete graph;
}
cout << "Testing done.\n";
ofstream os;
stringstream result;
result << string("data/HSMMmarginal_test_") << ID << string(".txt");
os.open(result.str().c_str(), ios::trunc);
for(size_t i=0; i<all_marginal.size(); i++){
for(size_t j=0; j<all_marginal[i].size(); j++){
os << all_marginal[i][j]<<" ";
}
os << "\n";
}
}
