35_test_regressor_ellipsoid.cpp

This is an example of how to use the netdem library.

#include "general_net.hpp"
#include "mlpack_utils.hpp"
#include "regression_net.hpp"
#include "utils_math.hpp"
 
using namespace netdem;
using namespace std;
 
void TestRegressorEllipsoid(int id_comp = 0) {
  string root_dir = "local/examples/netdem/ann_models/ellipsoid_ellipsoid/";
 
  arma::mat dataset;
  bool loadedDataset =
      mlpack::data::Load(root_dir + "dataset_resolution.txt", dataset, true);
  if (!loadedDataset) {
    cout << "loading data erros ..." << endl;
    return;
  }
  // element 4 and 5 are the volume and equivalent cross-section area
  // scale so that they are in the same unit space as particle size
  for (int i = 0; i < dataset.n_cols; i++) {
    auto tmp_sgn = Math::Sign(dataset(3, i));
    dataset(3, i) = tmp_sgn * dataset(3, i);
    dataset(4, i) = tmp_sgn * dataset(4, i);
    dataset(5, i) = tmp_sgn * dataset(5, i);
    dataset(6, i) = tmp_sgn * dataset(6, i);
    dataset(7, i) = dataset(7, i) * 40.0;
  }
  cout << "finished loading data ..." << endl;
 
  arma::mat test_data = dataset;
 
  int num_x{7}, num_y{7};
  arma::mat test_x = test_data.rows(0, num_x - 1);
  arma::mat test_y = test_data.rows(num_x, num_x + num_y - 1);
  cout << "finished preparing training and testing data ..." << endl;
 
  RegressionNet model;
  model.Load(root_dir + "ann_regressor.xml", "resolution");
  cout << "finished loading ann model ..." << endl;
 
  auto pred_y = model.Predict(test_x);
  double test_accuracy =
      MLPackUtils::GetMAE(pred_y.rows(id_comp, id_comp), test_y.rows(id_comp, id_comp));
  cout << "test mae: " << test_accuracy << endl;
 
  arma::mat joined_yy = std::move(arma::join_cols(pred_y, test_y));
  mlpack::data::Save(root_dir + "pred_vs_true_resolution.txt", joined_yy, false,
                     true, mlpack::data::FileType::CSVASCII);
}