bob::ip::gabor::JetStatistics::JetStatistics(bob::io::base::HDF5File& hdf5){
m_meanAbs.reference(hdf5.readArray<double,1>("MeanAbs"));
m_varAbs.reference(hdf5.readArray<double,1>("VarAbs"));
m_meanPhase.reference(hdf5.readArray<double,1>("MeanPhase"));
m_varPhase.reference(hdf5.readArray<double,1>("VarPhase"));
if (hdf5.hasGroup("Transform")){
hdf5.cd("Transform");
m_gwt.reset(new bob::ip::gabor::Transform(hdf5));
hdf5.cd("..");
}
}
void Machine::load (bob::io::base::HDF5File& config) {
//reads all data directly into the member variables
m_input_sub.reference(config.readArray<double,1>("input_sub"));
m_input_div.reference(config.readArray<double,1>("input_div"));
m_weight.reference(config.readArray<double,2>("weights"));
m_bias.reference(config.readArray<double,1>("biases"));
m_buffer.resize(m_input_sub.extent(0));
//switch between different versions - support for version 1
if (config.hasAttribute(".", "version")) { //new version
config.cd("activation");
m_activation = bob::learn::activation::load_activation(config);
config.cd("..");
}
else { //old version
uint32_t act = config.read<uint32_t>("activation");
m_activation = bob::learn::activation::make_deprecated_activation(act);
}
}
void bob::ip::gabor::JetStatistics::save(bob::io::base::HDF5File& hdf5, bool saveTransform) const{
hdf5.setArray("MeanAbs", m_meanAbs);
hdf5.setArray("VarAbs", m_varAbs);
hdf5.setArray("MeanPhase", m_meanPhase);
hdf5.setArray("VarPhase", m_varPhase);
if (saveTransform && m_gwt){
hdf5.createGroup("Transform");
hdf5.cd("Transform");
m_gwt->save(hdf5);
hdf5.cd("..");
}
}
void bob::learn::em::GMMMachine::load(bob::io::base::HDF5File& config) {
int64_t v;
v = config.read<int64_t>("m_n_gaussians");
m_n_gaussians = static_cast<size_t>(v);
v = config.read<int64_t>("m_n_inputs");
m_n_inputs = static_cast<size_t>(v);
m_gaussians.clear();
for(size_t i=0; i<m_n_gaussians; ++i) {
m_gaussians.push_back(boost::shared_ptr<bob::learn::em::Gaussian>(new bob::learn::em::Gaussian(m_n_inputs)));
std::ostringstream oss;
oss << "m_gaussians" << i;
config.cd(oss.str());
m_gaussians[i]->load(config);
config.cd("..");
}
m_weights.resize(m_n_gaussians);
config.readArray("m_weights", m_weights);
// Initialise cache
initCache();
}
void Machine::save (bob::io::base::HDF5File& config) const {
config.setAttribute(".", "version", 1);
config.setArray("input_sub", m_input_sub);
config.setArray("input_div", m_input_div);
config.setArray("weights", m_weight);
config.setArray("biases", m_bias);
config.createGroup("activation");
config.cd("activation");
m_activation->save(config);
config.cd("..");
}
void bob::learn::em::GMMMachine::save(bob::io::base::HDF5File& config) const {
int64_t v = static_cast<int64_t>(m_n_gaussians);
config.set("m_n_gaussians", v);
v = static_cast<int64_t>(m_n_inputs);
config.set("m_n_inputs", v);
for(size_t i=0; i<m_n_gaussians; ++i) {
std::ostringstream oss;
oss << "m_gaussians" << i;
if (!config.hasGroup(oss.str())) config.createGroup(oss.str());
config.cd(oss.str());
m_gaussians[i]->save(config);
config.cd("..");
}
config.setArray("m_weights", m_weights);
}
void bob::learn::boosting::StumpMachine::save(bob::io::base::HDF5File& file) const{
file.set("Threshold", m_threshold);
file.set("Polarity", m_polarity);
file.set("Index", m_index);
file.setAttribute(".", "MachineType", std::string("StumpMachine"));
}
