Method doCrossValidation

ml/src/svm_wrapper.cpp:69–118

}

void
pcl::SVMTrain::doCrossValidation()
{
  int total_correct = 0;
  double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
  double* target;

  // number of fold for the cross validation (n of folds = number of splitting of the
  // input dataset)
  if (nr_fold_ < 2) {
    fprintf(stderr, "n-fold cross validation: n must >= 2\n");
    return;
  }
  target = Malloc(double, prob_.l);

  svm_cross_validation(&prob_, &param_, nr_fold_, target); // perform cross validation

  if (param_.svm_type == EPSILON_SVR || param_.svm_type == NU_SVR) {
    double total_error = 0;
    for (int i = 0; i < prob_.l; i++) {
      double y = prob_.y[i];
      double v = target[i];
      total_error += (v - y) * (v - y);
      sumv += v;
      sumy += y;
      sumvv += v * v;
      sumyy += y * y;
      sumvy += v * y;
    }

    pcl::console::print_info(" - Cross Validation Mean squared error = ");
    pcl::console::print_value("%g\n", total_error / prob_.l);

    pcl::console::print_info(" - Cross Validation Squared correlation coefficient = ");
    pcl::console::print_value(
        "%g\n",
        ((prob_.l * sumvy - sumv * sumy) * (prob_.l * sumvy - sumv * sumy)) /
            ((prob_.l * sumvv - sumv * sumv) * (prob_.l * sumyy - sumy * sumy)));
  }
  else {
    for (int i = 0; i < prob_.l; i++)
      if (target[i] == prob_.y[i])
        ++total_correct;

    pcl::console::print_info(" - Cross Validation Accuracy = ");
    pcl::console::print_value("%g%%\n", 100.0 * total_correct / prob_.l);
  }

  free(target);
}

void
pcl::SVMTrain::scaleFactors(std::vector<SVMData> training_set, svm_scaling& scaling)