Function svm_predict_values

ml/src/svm.cpp:2842–2924

}

double
svm_predict_values(const svm_model* model, const svm_node* x, double* dec_values)
{
  if (model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR ||
      model->param.svm_type == NU_SVR) {
    double* sv_coef = model->sv_coef[0];
    double sum = 0;

    for (int i = 0; i < model->l; i++)
      sum += sv_coef[i] * Kernel::k_function(x, model->SV[i], model->param);

    sum -= model->rho[0];

    *dec_values = sum;

    if (model->param.svm_type == ONE_CLASS)
      return (sum > 0) ? 1 : -1;
    return sum;
  }

  int nr_class = model->nr_class;
  int l = model->l;

  double* kvalue = Malloc(double, l);

  for (int i = 0; i < l; i++)
    kvalue[i] = Kernel::k_function(x, model->SV[i], model->param);

  int* start = Malloc(int, nr_class);

  start[0] = 0;

  for (int i = 1; i < nr_class; i++)
    start[i] = start[i - 1] + model->nSV[i - 1];

  int* vote = Malloc(int, nr_class);

  for (int i = 0; i < nr_class; i++)
    vote[i] = 0;

  int p = 0;

  for (int i = 0; i < nr_class; i++)
    for (int j = i + 1; j < nr_class; j++) {
      double sum = 0;
      int si = start[i];
      int sj = start[j];
      int ci = model->nSV[i];
      int cj = model->nSV[j];

      double* coef1 = model->sv_coef[j - 1];
      double* coef2 = model->sv_coef[i];

      for (int k = 0; k < ci; k++)
        sum += coef1[si + k] * kvalue[si + k];

      for (int k = 0; k < cj; k++)
        sum += coef2[sj + k] * kvalue[sj + k];