#ifndef LIGHTGBM_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_
#define LIGHTGBM_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_

#include <LightGBM/meta.h>

#include <LightGBM/objective_function.h>
#include <LightGBM/utils/array_args.h>

namespace LightGBM {

#define PercentileFun(T, data_reader, cnt_data, alpha) {\
  std::vector<T> ref_data(cnt_data);\
  for (data_size_t i = 0; i < cnt_data; ++i) {\
    ref_data[i] = data_reader(i);\
  }\
  const double float_pos = (1.0f - alpha) * cnt_data;\
  const data_size_t pos = static_cast<data_size_t>(float_pos);\
  if (pos < 1) {\
    return ref_data[ArrayArgs<T>::ArgMax(ref_data)];\
  } else if (pos >= cnt_data) {\
    return ref_data[ArrayArgs<T>::ArgMin(ref_data)];\
  } else {\
    const double bias = float_pos - pos;\
    if (pos > cnt_data / 2) {\
      ArrayArgs<T>::ArgMaxAtK(&ref_data, 0, cnt_data, pos - 1);\
      T v1 = ref_data[pos - 1];\
      T v2 = ref_data[pos + ArrayArgs<T>::ArgMax(ref_data.data() + pos, cnt_data - pos)];\
      return static_cast<T>(v1 - (v1 - v2) * bias);\
    } else {\
      ArrayArgs<T>::ArgMaxAtK(&ref_data, 0, cnt_data, pos);\
      T v2 = ref_data[pos];\
      T v1 = ref_data[ArrayArgs<T>::ArgMin(ref_data.data(), pos)];\
      return static_cast<T>(v1 - (v1 - v2) * bias);\
    }\
  }\
}\

#define WeightedPercentileFun(T, data_reader, weight_reader, cnt_data, alpha) {\
  std::vector<data_size_t> sorted_idx(cnt_data);\
  for (data_size_t i = 0; i < cnt_data; ++i) {\
    sorted_idx[i] = i;\
  }\
  std::sort(sorted_idx.begin(), sorted_idx.end(), [=](data_size_t a, data_size_t b) {return data_reader(a) < data_reader(b); });\
  std::vector<double> weighted_cdf(cnt_data);\
  weighted_cdf[0] = weight_reader(sorted_idx[0]);\
  for (data_size_t i = 1; i < cnt_data; ++i) {\
    weighted_cdf[i] = weighted_cdf[i - 1] + weight_reader(sorted_idx[i]);\
  }\
  double threshold = weighted_cdf[cnt_data - 1] * alpha;\
  size_t pos = std::upper_bound(weighted_cdf.begin(), weighted_cdf.end(), threshold) - weighted_cdf.begin();\
  if (pos == 0) {\
    return data_reader(sorted_idx[0]);\
  }\
  CHECK(threshold >= weighted_cdf[pos - 1]);\
  CHECK(threshold < weighted_cdf[pos]);\
  T v1 = data_reader(sorted_idx[pos - 1]);\
  T v2 = data_reader(sorted_idx[pos]);\
  return static_cast<T>((threshold - weighted_cdf[pos]) / (weighted_cdf[pos + 1] - weighted_cdf[pos]) * (v2 - v1) + v1);\
}\

/*!
* \brief Objective function for regression
*/
class RegressionL2loss: public ObjectiveFunction {
public:
  explicit RegressionL2loss(const ObjectiveConfig& config) {
    sqrt_ = config.reg_sqrt;
  }

  explicit RegressionL2loss(const std::vector<std::string>& strs) {
    sqrt_ = false;
    for (auto str : strs) {
      if (str == std::string("sqrt")) {
        sqrt_ = true;
      }
    }
  }
  
  ~RegressionL2loss() {
  }

  void Init(const Metadata& metadata, data_size_t num_data) override {
    num_data_ = num_data;
    label_ = metadata.label();
    if (sqrt_) {
      trans_label_.resize(num_data_);
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data; ++i) {
        trans_label_[i] = Common::Sign(label_[i]) * std::sqrt(std::fabs(label_[i]));
      }
      label_ = trans_label_.data();
    }
    weights_ = metadata.weights();
  }

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>(score[i] - label_[i]);
        hessians[i] = 1.0f;
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>((score[i] - label_[i]) * weights_[i]);
        hessians[i] = static_cast<score_t>(weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "regression";
  }

  void ConvertOutput(const double* input, double* output) const override {
    if (sqrt_) {
      output[0] = Common::Sign(input[0]) * input[0] * input[0];
    } else {
      output[0] = input[0];
    }
  }

  std::string ToString() const override {
    std::stringstream str_buf;
    str_buf << GetName();
    if (sqrt_) {
      str_buf << " sqrt";
    }
    return str_buf.str();
  }

  bool IsConstantHessian() const override {
    if (weights_ == nullptr) {
      return true;
    } else {
      return false;
    }
  }

  double BoostFromScore() const override {
    double suml = 0.0f;
    double sumw = 0.0f;
    if (weights_ != nullptr) {
      #pragma omp parallel for schedule(static) reduction(+:suml,sumw)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i] * weights_[i];
        sumw += weights_[i];
      }
    } else {
      sumw = static_cast<double>(num_data_);
      #pragma omp parallel for schedule(static) reduction(+:suml)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i];
      }
    }
    return suml / sumw;
  }

protected:
  bool sqrt_;
  /*! \brief Number of data */
  data_size_t num_data_;
  /*! \brief Pointer of label */
  const label_t* label_;
  /*! \brief Pointer of weights */
  const label_t* weights_;
  std::vector<label_t> trans_label_;
};

/*!
* \brief L1 regression loss
*/
class RegressionL1loss: public RegressionL2loss {
public:
  explicit RegressionL1loss(const ObjectiveConfig& config): RegressionL2loss(config) {
  }

  explicit RegressionL1loss(const std::vector<std::string>& strs): RegressionL2loss(strs) {
  }

  ~RegressionL1loss() {}

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(Common::Sign(diff));
        hessians[i] = 1.0f;
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(Common::Sign(diff) * weights_[i]);
        hessians[i] = weights_[i];
      }
    }
  }

  double BoostFromScore() const override {
    const double alpha = 0.5;
    if (weights_ != nullptr) {
      #define data_reader(i) (label_[i])
      #define weight_reader(i) (weights_[i])
      WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha);
      #undef data_reader
      #undef weight_reader
    } else {
      #define data_reader(i) (label_[i])
      PercentileFun(label_t, data_reader, num_data_, alpha);
      #undef data_reader
    }
  }

  bool IsRenewTreeOutput() const override { return true; }

  double RenewTreeOutput(double, const double* pred, 
                         const data_size_t* index_mapper,
                         const data_size_t* bagging_mapper,
                         data_size_t num_data_in_leaf) const override {
    const double alpha = 0.5;
    if (weights_ == nullptr) {
      if (bagging_mapper == nullptr) {
        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
        PercentileFun(double, data_reader, num_data_in_leaf, alpha);
        #undef data_reader
      } else {
        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
        PercentileFun(double, data_reader, num_data_in_leaf, alpha);
        #undef data_reader
      }
    } else {
      if (bagging_mapper == nullptr) {
        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
        #define weight_reader(i) (weights_[index_mapper[i]])
        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
        #undef data_reader
        #undef weight_reader
      } else {
        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
        #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
        #undef data_reader
        #undef weight_reader
      }
    }
  }

  const char* GetName() const override {
    return "regression_l1";
  }
};

/*!
* \brief Huber regression loss
*/
class RegressionHuberLoss: public RegressionL2loss {
public:
  explicit RegressionHuberLoss(const ObjectiveConfig& config): RegressionL2loss(config) {
    alpha_ = static_cast<double>(config.alpha);
  }

  explicit RegressionHuberLoss(const std::vector<std::string>& strs): RegressionL2loss(strs) {

  }

  ~RegressionHuberLoss() {
  }

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        if (std::abs(diff) <= alpha_) {
          gradients[i] = static_cast<score_t>(diff);
        } else {
          gradients[i] = static_cast<score_t>(Common::Sign(diff) * alpha_);
        }
        hessians[i] = 1.0f;
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        if (std::abs(diff) <= alpha_) {
          gradients[i] = static_cast<score_t>(diff * weights_[i]);
        } else {
          gradients[i] = static_cast<score_t>(Common::Sign(diff) * weights_[i] * alpha_);
        }
        hessians[i] = static_cast<score_t>(weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "huber";
  }

  bool IsConstantHessian() const override {
    return false;
  }

private:
  /*! \brief delta for Huber loss */
  double alpha_;
};


// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html
class RegressionFairLoss: public RegressionL2loss {
public:
  explicit RegressionFairLoss(const ObjectiveConfig& config): RegressionL2loss(config) {
    c_ = static_cast<double>(config.fair_c);
  }

  explicit RegressionFairLoss(const std::vector<std::string>& strs): RegressionL2loss(strs) {

  }

  ~RegressionFairLoss() {}

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double x = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(c_ * x / (std::fabs(x) + c_));
        hessians[i] = static_cast<score_t>(c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_)));
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double x = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(c_ * x / (std::fabs(x) + c_) * weights_[i]);
        hessians[i] = static_cast<score_t>(c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_)) * weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "fair";
  }

  bool IsConstantHessian() const override {
    return false;
  }

private:
  /*! \brief c for Fair loss */
  double c_;
};


/*!
* \brief Objective function for Poisson regression
*/
class RegressionPoissonLoss: public RegressionL2loss {
public:
  explicit RegressionPoissonLoss(const ObjectiveConfig& config): RegressionL2loss(config) {
    max_delta_step_ = static_cast<double>(config.poisson_max_delta_step);
    if (sqrt_) {
      Log::Warning("cannot use sqrt transform in %s Regression, will auto disable it.", GetName());
      sqrt_ = false;
    }
  }

  explicit RegressionPoissonLoss(const std::vector<std::string>& strs): RegressionL2loss(strs) {

  }

  ~RegressionPoissonLoss() {}

  void Init(const Metadata& metadata, data_size_t num_data) override {
    if (sqrt_) {
      Log::Warning("cannot use sqrt transform in %s Regression, will auto disable it.", GetName());
      sqrt_ = false;
    }
    RegressionL2loss::Init(metadata, num_data);
    // Safety check of labels
    label_t miny;
    double sumy;
    Common::ObtainMinMaxSum(label_, num_data_, &miny, (label_t*)nullptr, &sumy);
    if (miny < 0.0f) {
      Log::Fatal("[%s]: at least one target label is negative.", GetName());
    }
    if (sumy == 0.0f) {
      Log::Fatal("[%s]: sum of labels is zero.", GetName());
    }
  }

  /* Parametrize with unbounded internal score "f"; then
   *  loss = exp(f) - label * f
   *  grad = exp(f) - label
   *  hess = exp(f)
   *
   * And the output is exp(f); so the associated metric get s=exp(f)
   * so that its loss = s - label * log(s); a little awkward maybe.
   *
   */
  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>(std::exp(score[i]) - label_[i]);
        hessians[i] = static_cast<score_t>(std::exp(score[i] + max_delta_step_));
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>((std::exp(score[i]) - label_[i]) * weights_[i]);
        hessians[i] = static_cast<score_t>(std::exp(score[i] + max_delta_step_) * weights_[i]);
      }
    }
  }

  void ConvertOutput(const double* input, double* output) const override {
    output[0] = std::exp(input[0]);
  }

  const char* GetName() const override {
    return "poisson";
  }

  double BoostFromScore() const override {
    return std::log(RegressionL2loss::BoostFromScore());
  }

  bool IsConstantHessian() const override {
    return false;
  }

private:
  /*! \brief used to safeguard optimization */
  double max_delta_step_;
};

class RegressionQuantileloss : public RegressionL2loss {
public:
  explicit RegressionQuantileloss(const ObjectiveConfig& config): RegressionL2loss(config) {
    alpha_ = static_cast<score_t>(config.alpha);
    CHECK(alpha_ > 0 && alpha_ < 1);
  }

  explicit RegressionQuantileloss(const std::vector<std::string>& strs): RegressionL2loss(strs) {

  }

  ~RegressionQuantileloss() {}

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        score_t delta = static_cast<score_t>(score[i] - label_[i]);
        if (delta >= 0) {
          gradients[i] = (1.0f - alpha_);
        } else {
          gradients[i] = -alpha_;
        }
        hessians[i] = 1.0f;
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        score_t delta = static_cast<score_t>(score[i] - label_[i]);
        if (delta >= 0) {
          gradients[i] = static_cast<score_t>((1.0f - alpha_) * weights_[i]);
        } else {
          gradients[i] = static_cast<score_t>(-alpha_ * weights_[i]);
        }
        hessians[i] = static_cast<score_t>(weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "quantile";
  }

  double BoostFromScore() const override {
    if (weights_ != nullptr) {
      #define data_reader(i) (label_[i])
      #define weight_reader(i) (weights_[i])
      WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha_);
      #undef data_reader
      #undef weight_reader
    } else {
      #define data_reader(i) (label_[i])
      PercentileFun(label_t, data_reader, num_data_, alpha_);
      #undef data_reader
    }
  }

  bool IsRenewTreeOutput() const override { return true; }

  double RenewTreeOutput(double, const double* pred,
                         const data_size_t* index_mapper,
                         const data_size_t* bagging_mapper,
                         data_size_t num_data_in_leaf) const override {
    if (weights_ == nullptr) {
      if (bagging_mapper == nullptr) {
        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
        PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
        #undef data_reader
      } else {
        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
        PercentileFun(double, data_reader, num_data_in_leaf, alpha_);
        #undef data_reader
      }
    } else {
      if (bagging_mapper == nullptr) {
        #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
        #define weight_reader(i) (weights_[index_mapper[i]])
        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
        #undef data_reader
        #undef weight_reader
      } else {
        #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
        #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]])
        WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_);
        #undef data_reader
        #undef weight_reader
      }
    }
  }

private:
  score_t alpha_;
};


/*!
* \brief Mape Regression Loss
*/
class RegressionMAPELOSS : public RegressionL1loss {
public:
  explicit RegressionMAPELOSS(const ObjectiveConfig& config) : RegressionL1loss(config) {
  }

  explicit RegressionMAPELOSS(const std::vector<std::string>& strs) : RegressionL1loss(strs) {

  }

  ~RegressionMAPELOSS() {}

  void Init(const Metadata& metadata, data_size_t num_data) override {
    RegressionL2loss::Init(metadata, num_data);
    for (data_size_t i = 0; i < num_data_; ++i) {
      if (std::fabs(label_[i]) < 1) {
        Log::Warning("Met 'abs(label) < 1', will convert them to '1' in Mape objective and metric.");
        break;
      }
    }
    label_weight_.resize(num_data);
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        label_weight_[i] = 1.0f / std::max(1.0f, std::fabs(label_[i]));
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        label_weight_[i] = 1.0f / std::max(1.0f, std::fabs(label_[i])) * weights_[i];
      }
    }
  }

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(Common::Sign(diff) * label_weight_[i]);
        hessians[i] = 1.0f;
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double diff = score[i] - label_[i];
        gradients[i] = static_cast<score_t>(Common::Sign(diff) * label_weight_[i]);
        hessians[i] = weights_[i];
      }
    }
  }

  double BoostFromScore() const override {
    const double alpha = 0.5;
    #define data_reader(i) (label_[i])
    #define weight_reader(i) (label_weight_[i])
    WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha);
    #undef data_reader
    #undef weight_reader
  }

  bool IsRenewTreeOutput() const override { return true; }

  double RenewTreeOutput(double, const double* pred,
                         const data_size_t* index_mapper,
                         const data_size_t* bagging_mapper,
                         data_size_t num_data_in_leaf) const override {
    const double alpha = 0.5;
    if (bagging_mapper == nullptr) {
      #define data_reader(i) (label_[index_mapper[i]] - pred[index_mapper[i]])
      #define weight_reader(i) (label_weight_[index_mapper[i]])
      WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
      #undef data_reader
      #undef weight_reader
    } else {
      #define data_reader(i) (label_[bagging_mapper[index_mapper[i]]] - pred[bagging_mapper[index_mapper[i]]])
      #define weight_reader(i) (label_weight_[bagging_mapper[index_mapper[i]]])
      WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha);
      #undef data_reader
      #undef weight_reader
    }
  }

  const char* GetName() const override {
    return "mape";
  }

  bool IsConstantHessian() const override {
    return true;
  }

private:
  std::vector<label_t> label_weight_;

};



/*!
* \brief Objective function for Gamma regression
*/
class RegressionGammaLoss : public RegressionPoissonLoss {
public:
  explicit RegressionGammaLoss(const ObjectiveConfig& config) : RegressionPoissonLoss(config) {
  }

  explicit RegressionGammaLoss(const std::vector<std::string>& strs) : RegressionPoissonLoss(strs) {

  }

  ~RegressionGammaLoss() {}

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>(1.0 - label_[i] / std::exp(score[i]));
        hessians[i] = static_cast<score_t>(label_[i] / std::exp(score[i]));
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>(1.0 - label_[i] / std::exp(score[i]) * weights_[i]);
        hessians[i] = static_cast<score_t>(label_[i] / std::exp(score[i]) * weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "gamma";
  }
 
};

/*!
* \brief Objective function for Tweedie regression
*/
class RegressionTweedieLoss: public RegressionPoissonLoss {
public:
  explicit RegressionTweedieLoss(const ObjectiveConfig& config) : RegressionPoissonLoss(config) {
    rho_ = config.tweedie_variance_power;
  }

  explicit RegressionTweedieLoss(const std::vector<std::string>& strs) : RegressionPoissonLoss(strs) {

  }

  ~RegressionTweedieLoss() {}

  void GetGradients(const double* score, score_t* gradients,
                    score_t* hessians) const override {
    if (weights_ == nullptr) {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>(-label_[i] * std::exp((1 - rho_) * score[i]) + std::exp((2 - rho_) * score[i]));
        hessians[i] = static_cast<score_t>(-label_[i] * (1 - rho_) * std::exp((1 - rho_) * score[i]) + 
          (2 - rho_) * std::exp((2 - rho_) * score[i]));
      }
    } else {
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        gradients[i] = static_cast<score_t>((-label_[i] * std::exp((1 - rho_) * score[i]) + std::exp((2 - rho_) * score[i])) * weights_[i]);
        hessians[i] = static_cast<score_t>((-label_[i] * (1 - rho_) * std::exp((1 - rho_) * score[i]) +
          (2 - rho_) * std::exp((2 - rho_) * score[i])) * weights_[i]);
      }
    }
  }

  const char* GetName() const override {
    return "tweedie";
  }
private:
  double rho_;
};

#undef PercentileFun
#undef WeightedPercentileFun

}  // namespace LightGBM
#endif   // LightGBM_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_
