int main(int n_args, char** args)
{
double intersection = 0.5;
double n_rfs = 9;
string fa_name = string(args[1]);
string directory = string(args[2]);
if (n_args>3)
intersection = atof(args[3]);
if (n_args>4)
n_rfs = atoi(args[4]);
// Load training data
MatrixXd inputs;
MatrixXd targets;
directory += "/";
if (!loadMatrix(directory+"inputs.txt", inputs)) return -1;
if (!loadMatrix(directory+"targets.txt", targets)) return -1;
int input_dim = inputs.cols();
// Initialize function approximator
FunctionApproximator* fa;
if (fa_name.compare("LWR")==0)
{
MetaParametersLWR* meta_params = new MetaParametersLWR(input_dim,n_rfs,intersection);
fa = new FunctionApproximatorLWR(meta_params);
}
else
{
MetaParametersRBFN* meta_params = new MetaParametersRBFN(input_dim,n_rfs,intersection);
fa = new FunctionApproximatorRBFN(meta_params);
}
// Train function approximator with data
bool overwrite = true;
fa->train(inputs,targets,directory,overwrite);
// Make predictions for the targets
MatrixXd outputs(inputs.rows(),fa->getExpectedOutputDim());
fa->predict(inputs,outputs);
saveMatrix(directory,"outputs.txt",outputs,overwrite);
VectorXd min(1); min << 0.0;
VectorXd max(1); max << 2.0;
VectorXi n_samples_grid(1); n_samples_grid << 201;
fa->saveGridData(min, max, n_samples_grid, directory, overwrite);
delete fa;
return 0;
}
