/**
 * @file eqpalg/oneClassSvm/frame.h
 * @brief oneClassSvm的框架
 * @author Cat (null.null.null@qq.com)
 * @version 0.1
 * @date 2021-07-08
 *
 * Company: Baosight Co. Ltd.
 * DO NOT COPY/USE WITHOUT PERMISSION
 *
 */
#pragma once

#include <iostream>
#include <dlib/svm.h>
#include <log4cplus/LOG.h>
#include <chrono>
#include <utility>
#include <string>
//#include <dlib/svm.h>
#include <dlib/array2d.h>
#include <vector>
#include <tuple>
// #include <eqpalg/regression/krls.h>
// #include <eqpalg/regression/krr.h>
// #include <eqpalg/regression/train_result.h>
//#include <eqpalg/oneClassSvm/oneSVM.hpp>

namespace oneClassSvm {

  using std::tuple;
  using std::vector;
  using namespace dlib;
  using sample_type = matrix<double, 0, 1>;
  using kernel_type = radial_basis_kernel<sample_type>;
  using TrainResult = decision_function<kernel_type>;
  using SamplesType = vector<sample_type>;
  TrainResult decision_function_get(SamplesType samples);

/**
 * @brief one class svm的主框架
 * @tparam rows
 */
template <size_t dims>
struct Frame {
  using ExchangedMetaData =
      std::array<double, dims>;  ///< 用来做数据输入的单元数据类型
  using InData =
      std::vector<ExchangedMetaData>;  ///< 用来做数据输入的数组数据类型



  Frame(const std::string& ruleId) { this->rule_id_ = ruleId; }

  TrainResult svm_result_;  ///< 训练得到的svm判别函数

 private:
  std::string rule_id_;  //< 算法实例id
 public:
  /**
   * @brief Construct a new Frame object
   * @param  ruleId           My Param doc
   */
  // Frame(const std::string& ruleId) { this->rule_id_ = ruleId; }

  /**
   * @brief Destroy the Frame object
   */
  ~Frame() = default;

  /**
   * @brief 载入用来训练的数据
   * @param  data             My Param doc
   * @return int
   */
  int load(const InData& data);

 protected:
  /**
   * @brief 训练svm模型
   * @param  tr_samples       样本-x
   * @return df函数
   */
  TrainResult& train_get_df_one_class_svm(SamplesType& tr_samples);

 public:
  /**
   * @brief 预测svm的结果
   * @param  data             输入-x
   * @return double
   */
  double predict(ExchangedMetaData& data);
  double standard_error;  //< 误差标准 train_get_df_one_class_svm后得到

  /**
   * @brief valid
   */
  bool valid();
};

}  // namespace oneClassSvm