int main(int n_args, char** args)
{
string directory;
if (n_args>1)
directory = string(args[1]);
//else
// usage(args[0],"/tmp/testFunctionApproximatorLWR");
for (int input_dim=1; input_dim<=2; input_dim++)
{
cout << "________________________________________________________________________" << endl;
cout << "________________________________________________________________________" << endl;
VectorXi n_samples_per_dim = VectorXi::Constant(1,10);
if (input_dim==2)
n_samples_per_dim = VectorXi::Constant(2,25);
MatrixXd inputs, targets, outputs;
targetFunction(n_samples_per_dim,inputs,targets);
double intersection = 0.5;
int n_rfs = 9;
if (input_dim==2)
n_rfs = 3;
VectorXi num_rfs_per_dim = VectorXi::Constant(input_dim,n_rfs);
MetaParametersLWR* meta_parameters = new MetaParametersLWR(input_dim,num_rfs_per_dim,intersection);
string save_directory;
if (!directory.empty())
save_directory = directory+"/"+(input_dim==1?"1D":"2D");
FunctionApproximator* fa = new FunctionApproximatorLWR(meta_parameters);
bool overwrite = true;
fa->train(inputs,targets,save_directory,overwrite);
// Now the basic functionality of the LWR FA has been tested.
// No perturb the model parameters
const ModelParametersLWR* model_parameters_lwr_const = static_cast< const ModelParametersLWR*>(fa->getModelParameters());
// Get a clone which is not const so that we can modify it
ModelParametersLWR* model_parameters_lwr = static_cast< ModelParametersLWR*>(model_parameters_lwr_const->clone());
set<string> selected;
selected.insert("offsets");
selected.insert("slopes");
model_parameters_lwr->setSelectedParameters(selected);
model_parameters_lwr->set_lines_pivot_at_max_activation(true);
VectorXd values;
bool normalized = false;
model_parameters_lwr->getParameterVectorSelected(values,normalized);
cout << "Original values : " << fixed << setprecision(4) << values.transpose() << endl;
normalized = true;
model_parameters_lwr->getParameterVectorSelected(values,normalized);
cout << "Original values (normalized): " << fixed << setprecision(4) << values.transpose() << endl;
normalized = true;
model_parameters_lwr->getParameterVectorSelected(values,normalized);
int n_perturbations = 5;
for (int i_perturbation=0; i_perturbation<n_perturbations; i_perturbation++)
{
if (!save_directory.empty())
{
// Get min and max of the targetfunction (i.e. generate just 2 samples per dimension)
MatrixXd inputs;
MatrixXd targets;
targetFunction(VectorXi::Constant(input_dim,2), inputs, targets);
VectorXd min = inputs.colwise().minCoeff();
VectorXd max = inputs.colwise().maxCoeff();
VectorXi n_samples_per_dim = VectorXi::Constant(input_dim,100);
if (input_dim==2)
n_samples_per_dim = VectorXi::Constant(input_dim,40);
string cur_save_directory = save_directory + "/perturbation" + to_string(i_perturbation);
FunctionApproximatorLWR fa(model_parameters_lwr);
fa.saveGridData(min, max, n_samples_per_dim, cur_save_directory, overwrite);
}
double scale = 0.05;
VectorXd perturbations = scale*VectorXd::Random(values.size());
VectorXd values_perturbed = values.array()+perturbations.array();
model_parameters_lwr->setParameterVectorSelected(values_perturbed,normalized);
//cout << *model_parameters_lwr << endl;
cout << "Perturbation " << i_perturbation << ": " << fixed << setprecision(4) << values_perturbed.transpose() << endl;
}
delete meta_parameters;
delete fa;
}
return 0;
}
