#include <eqpalg/regression/frame.h>
namespace regression {

Frame::Frame(const std::string& ruleId, size_t dims)
    : rule_id_(ruleId), dims_(dims) {}

int Frame::load(const SampleWindow& data) {
  TrSample tr_samples;
  Label tr_labels;
  // 如果不存在数据，退出
  if (data.empty()) {
    return -1;
  }
  // 提前保留样本
  tr_samples.reserve(data.size());
  tr_labels.reserve(data.size());
  // 把输入转成dlib样本
  for (const auto& meta_data : data) {
    SampleType tmp(dims_,1);
    for (size_t i = 0; i < meta_data.size() - 1; i++) {
      tmp(i) = meta_data[i];
    }
    tr_samples.emplace_back(tmp);

    tr_labels.push_back(meta_data[meta_data.size() - 1]);
  }
  // 计算gama
  auto learn_sample_size = tr_samples.size() * 0.1;
  const double gama = 3.0 / compute_mean_squared_distance(randomly_subsample(
                                tr_samples, learn_sample_size * 0.1));
  this->train_and_test_regression(gama, tr_samples, tr_labels);
  return 0;
}

int Frame::train_and_test_regression(const double gama,
                                      const TrSample& tr_samples,
                                      const Label& tr_labels) {
  // 计算krr回归
  krr_ = std::make_unique<Krr>(dims_, gama, tr_samples, tr_labels);
  best_regression_type = 1;

  return 0;
}

double Frame::predict(const SamplePoint& data) { return krr_->predict(data); }

bool Frame::valid() {
  if (this->best_regression_type > 0) {
    return true;
  }
  return false;
}
}  // namespace regression