Function svm_train

ml/src/svm.cpp:2402–2674

Interface functions

// Interface functions
//
svm_model*
svm_train(const svm_problem* prob, const svm_parameter* param)
{
  auto* model = Malloc(svm_model, 1);
  model->param = *param;
  model->free_sv = 0; // XXX
  model->probA = nullptr;
  model->probB = nullptr;

  if (param->svm_type == ONE_CLASS || param->svm_type == EPSILON_SVR ||
      param->svm_type == NU_SVR) {
    // regression or one-class-svm
    model->nr_class = 2;
    model->label = nullptr;
    model->nSV = nullptr;
    model->probA = nullptr;
    model->probB = nullptr;
    model->sv_coef = Malloc(double*, 1);

    if (param->probability &&
        (param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR)) {
      model->probA = Malloc(double, 1);
      model->probA[0] = svm_svr_probability(prob, param);
    }

    decision_function f = svm_train_one(prob, param, 0, 0);

    model->rho = Malloc(double, 1);
    model->rho[0] = f.rho;

    int nSV = 0;

    for (int i = 0; i < prob->l; i++)
      if (std::abs(f.alpha[i]) > 0)
        ++nSV;

    model->l = nSV;

    model->SV = Malloc(svm_node*, nSV);

    model->sv_coef[0] = Malloc(double, nSV);

    int j = 0;

    for (int i = 0; i < prob->l; i++)
      if (std::abs(f.alpha[i]) > 0) {
        model->SV[j] = prob->x[i];
        model->sv_coef[0][j] = f.alpha[i];
        ++j;
      }

    free(f.alpha);
  }
  else {
    // classification
    int l = prob->l;
    int nr_class;
    int* label = nullptr;