Method trainClassifier

ml/src/svm_wrapper.cpp:225–268

};

bool
pcl::SVMTrain::trainClassifier()
{
  if (training_set_.empty()) {
    // to be sure to have loaded the training set
    PCL_ERROR("[pcl::%s::trainClassifier] Training data not set!\n",
              getClassName().c_str());
    return false;
  }

  scaleFactors(training_set_, scaling_);
  adaptInputToLibSVM(training_set_, prob_);

  const char* error_msg;
  error_msg = svm_check_parameter(&prob_, &param_);

  // initialize gamma parameter

  if (param_.gamma == 0 && scaling_.max > 0)
    param_.gamma = 1.0 / scaling_.max;

  if (error_msg) {
    PCL_ERROR("[pcl::%s::trainClassifier] %s\n", getClassName().c_str(), error_msg);
    // fprintf (stderr, "ERROR: %s\n", error_msg);
    exit(1);
  }

  if (cross_validation_) {
    doCrossValidation();
  }
  else {
    auto* out = reinterpret_cast<SVMModel*>(svm_train(&prob_, &param_));
    if (out == nullptr) {
      PCL_ERROR("[pcl::%s::trainClassifier] Error taining the classifier model.\n",
                getClassName().c_str());
      return false;
    }
    model_ = *out;
    model_.scaling = scaling_.obj;
    free(out);
  }

  return true;
}

bool
pcl::SVM::loadProblem(const char* filename, svm_problem& prob)