void RBSCMConstruction::process_parameters_file(const std::string& parameters_filename)
{
// First read in data from parameters_filename
GetPot infile(parameters_filename);
const unsigned int n_training_samples = infile("n_training_samples",1);
const bool deterministic_training = infile("deterministic_training",false);
// Read in training_parameters_random_seed value. This is used to
// seed the RNG when picking the training parameters. By default the
// value is -1, which means use std::time to seed the RNG.
unsigned int training_parameters_random_seed_in = static_cast<unsigned int>(-1);
training_parameters_random_seed_in = infile("training_parameters_random_seed",
training_parameters_random_seed_in);
set_training_random_seed(training_parameters_random_seed_in);
// SCM Greedy termination tolerance
const Real SCM_training_tolerance_in = infile("SCM_training_tolerance", SCM_training_tolerance);
set_SCM_training_tolerance(SCM_training_tolerance_in);
// Initialize the parameter ranges and the parameters themselves
initialize_parameters(parameters_filename);
std::map<std::string,bool> log_scaling;
const RBParameters& mu = get_parameters();
RBParameters::const_iterator it = mu.begin();
RBParameters::const_iterator it_end = mu.end();
unsigned int i=0;
for( ; it != it_end; ++it)
{
// Read vector-based log scaling values. Note the intermediate conversion to
// int... this implies log_scaling = '1 1 1...' in the input file.
// log_scaling[i] = static_cast<bool>(infile("log_scaling", static_cast<int>(log_scaling[i]), i));
std::string param_name = it->first;
log_scaling[param_name] = static_cast<bool>(infile("log_scaling", 0, i));
i++;
}
initialize_training_parameters(this->get_parameters_min(),
this->get_parameters_max(),
n_training_samples,
log_scaling,
deterministic_training); // use deterministic parameters
}
void RBSCMConstruction::process_parameters_file(const std::string& parameters_filename)
{
// First read in data from parameters_filename
GetPot infile(parameters_filename);
const unsigned int n_training_samples = infile("n_training_samples",1);
const bool deterministic_training = infile("deterministic_training",false);
// Read in training_parameters_random_seed value. This is used to
// seed the RNG when picking the training parameters. By default the
// value is -1, which means use std::time to seed the RNG.
unsigned int training_parameters_random_seed_in = static_cast<int>(-1);
training_parameters_random_seed_in = infile("training_parameters_random_seed",
training_parameters_random_seed_in);
set_training_random_seed(training_parameters_random_seed_in);
// SCM Greedy termination tolerance
const Real SCM_training_tolerance_in = infile("SCM_training_tolerance", SCM_training_tolerance);
set_SCM_training_tolerance(SCM_training_tolerance_in);
// Initialize the parameter ranges and the parameters themselves
unsigned int n_continuous_parameters = infile.vector_variable_size("parameter_names");
RBParameters mu_min_in;
RBParameters mu_max_in;
for(unsigned int i=0; i<n_continuous_parameters; i++)
{
// Read in the parameter names
std::string param_name = infile("parameter_names", "NONE", i);
{
Real min_val = infile(param_name, 0., 0);
mu_min_in.set_value(param_name, min_val);
}
{
Real max_val = infile(param_name, 0., 1);
mu_max_in.set_value(param_name, max_val);
}
}
std::map< std::string, std::vector<Real> > discrete_parameter_values_in;
unsigned int n_discrete_parameters = infile.vector_variable_size("discrete_parameter_names");
for(unsigned int i=0; i<n_discrete_parameters; i++)
{
std::string param_name = infile("discrete_parameter_names", "NONE", i);
unsigned int n_vals_for_param = infile.vector_variable_size(param_name);
std::vector<Real> vals_for_param(n_vals_for_param);
for(unsigned int j=0; j<vals_for_param.size(); j++)
{
vals_for_param[j] = infile(param_name, 0., j);
}
discrete_parameter_values_in[param_name] = vals_for_param;
}
initialize_parameters(mu_min_in, mu_max_in, discrete_parameter_values_in);
std::map<std::string,bool> log_scaling;
const RBParameters& mu = get_parameters();
RBParameters::const_iterator it = mu.begin();
RBParameters::const_iterator it_end = mu.end();
unsigned int i=0;
for( ; it != it_end; ++it)
{
std::string param_name = it->first;
log_scaling[param_name] = static_cast<bool>(infile("log_scaling", 0, i));
i++;
}
initialize_training_parameters(this->get_parameters_min(),
this->get_parameters_max(),
n_training_samples,
log_scaling,
deterministic_training); // use deterministic parameters
}
