Method classification

ml/src/svm_wrapper.cpp:654–734

}

bool
pcl::SVMClassify::classification()
{
  if (model_.l == 0) {
    PCL_ERROR("[pcl::%s::classification] Classifier model has no data.\n",
              getClassName().c_str());
    return false;
  }

  if (prob_.l == 0) {
    PCL_ERROR("[pcl::%s::classification] Input dataset has no data.\n",
              getClassName().c_str());
    return false;
  }

  if (predict_probability_) {
    if (svm_check_probability_model(&model_) == 0) {
      PCL_WARN(
          "[pcl::%s::classification] Classifier model does not support probability "
          "estimates. Automatically disabled.\n",
          getClassName().c_str());
      predict_probability_ = false;
    }
  }
  else {
    if (svm_check_probability_model(&model_) != 0)
      PCL_WARN("[pcl::%s::classification] Classifier model supports probability "
               "estimates, but disabled in prediction.\n",
               getClassName().c_str());
  }

  int svm_type = svm_get_svm_type(&model_);
  int nr_class = svm_get_nr_class(&model_);

  double* prob_estimates = nullptr;

  prediction_.clear();

  if (predict_probability_) {
    if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
      PCL_WARN("[pcl::%s::classificationTest] Prob. model for test data: target value "
               "= predicted value + z,\nz: Laplace distribution "
               "e^(-|z|/sigma)/(2sigma),sigma=%g\n",
               getClassName().c_str(),
               svm_get_svr_probability(&model_));
    else {
      prob_estimates = static_cast<double*>(malloc(nr_class * sizeof(double)));
    }
  }

  int ii = 0;

  prediction_.resize(prob_.l);

  while (ii < prob_.l) {
    double predict_label;
    // char *idx, *val, *endptr;
    // int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed