#pragma once
/**
 * @file eqpalg/stat_tools/frame.h
 * @brief 所有统计相关的工具集合框架
 * @author Cat (null.null.null@qq.com)
 * @version 0.1
 * @date 2021-08-25
 * 所有统计相关的工具集合框架
 * 包含
 * 1. 数据存储和获得（data_handler)
 * 2. 数据回归性验证（regression）
 * 3. 数据分布验证（distribution）
 * Copyright: Baosight Co. Ltd.
 * DO NOT COPY/USE WITHOUT PERMISSION
 *
 */
#include "mix_cc/sql.h"
#include "mix_cc/sql/database/db2_t.h"
#include <boost/date_time.hpp>
#include <dlib/matrix.h>
#include <eqpalg/data_handler/frame.h>
#include <eqpalg/distribution/frame.h>
#include <eqpalg/gb_logger.h>
#include <eqpalg/oneClassSvm/frame.hpp>
#include <eqpalg/regression/frame.h>
#include <eqpalg/stat_tools/stat_alarm.h>
#include <eqpalg/utility/normalize.h>
#include <algorithm>
#include <filesystem>
#include <fstream>
#include <optional>
#include <random>
#include <string>
#include <utility>
#include <vector>

namespace stat_tools {
/**
 * @brief 统计报警模式
 */
enum class TestMode {
  abs_diff = 0,          ///< 绝对差
  percent_diff = 1,      ///< 误差百分比
  normal_dist_diff = 2,  ///< 正态置信
  degrad = 3,            ///< 劣化分析
  regression = 4,        ///< 多项式分析
  cluster = 5,           ///< DBSCAN聚类
  oneClassSvm = 6,       ///< 单类svm
  wave_test = 10,        ///< 波动检测
  wave_test_2 = 11       ///< 波动检测2
};

/**
 * @brief 统计报警框架
 */
struct Frame {
  using RunningScalaCovariance = dlib::running_scalar_covariance<double>;

  using TimePoint = std::chrono::system_clock::time_point;

 public:
  /**
   * @brief 设置数据归档周期
   * @param  interval         归档周期
   * @return int
   */
  int set_archive_interval(std::chrono::system_clock::duration interval);

  /**
   * @brief 设置评价参数
   * @param  prob             评价参数
   * @return int
   */
  int set_prob(double p_);

 protected:
  TimePoint time_begin_,
      time_end_ = system_clock::now();  ///< 查询的开始-结束时间
  TimePoint last_save_time_ = system_clock::now();   ///< 上次存储时间
  TimePoint last_alarm_time_ = system_clock::now();  ///< 上次报警时间

 public:
  static constexpr uint64_t min_judge_size =
      100;  ///< 最小的可以用来进行评鉴的数据量

 public:
  Frame(std::string ruleId, std::string rule_name, size_t dims,
        TestMode test_mode, TimePoint time_begin, TimePoint time_end,
        double padding_low = 0, double padding_up = 0,
        bool no_down_limit = false);

  ~Frame() = default;

  /**
   * @brief 根据不同的模式，从不同的数据来源中载入数据，并初始化判断方法
   * @return int
   */
  int load_data();

  /**
   * @brief 把数据变更提交到数据库和文件
   * @return int
   */
  int commit();

  /**
   * @brief 周期执行采样
   * @param  input_data       输入的样本
   * @return true
   * @return false
   */
  bool cron_sampling_data(const SampleWindow& input_data, TimePoint tp);

  /**
   * @brief 是否是第一次采样
   * @return true
   * @return false
   */
  bool is_first_sampling();

  /**
   * @brief 保存报警
   * @param  sample           My Param doc
   * @param  time_stamp       My Param doc
   * @return int
   */
  int save_alarm(SamplePoint sample, TimePoint time_stamp);

  /**
   * @brief 存储信息
   * @param  sample           My Param doc
   * @param  time_stamp       My Param doc
   * @return int
   */
  int store_data(SamplePoint sample, TimePoint time_stamp);

  /**
   * @brief 自动检测是否报警并存储
   * @param  sample           My Param doc
   * @param  time_stamp       My Param doc
   * @return StatAlarm
   */
  StatAlarm auto_detect_and_save(SamplePoint sample, TimePoint time_stamp);
  /**
   * @brief task normal
   */
  StatAlarm get_task_normal_info(SampleWindow samples);
  std::vector<std::vector<double>> get_dist_param() {
    return this->dist_param_;
  }

 protected:
  /**
   * @brief 报警存储实现
   * @param  sample_test      My Param doc
   * @param  time_stamp       My Param doc
   * @return int
   */
  int save_alarm_impl(SamplePoint sample_test, TimePoint time_stamp);

  /**
   * @brief 数据满足提交条件
   * @return true
   * @return false
   */
  bool ready_to_commit();

  /**
   * @brief 是否报警的实现
   * @param  sample           My Param doc
   * @return StatAlarm
   */
  StatAlarm is_alarmed_impl(SamplePoint sample);

 protected:
  /**
   * @brief 绝对差报警
   * @param  sample_test      My Param doc
   * @return StatAlarm
   */
  StatAlarm absolute_diff_alarm(SamplePoint sample_test);

  /**
   * @brief 百分比报警
   * @param  sample_test      My Param doc
   * @return StatAlarm
   */
  StatAlarm percentage_diff_alarm(SamplePoint sample_test);

  /**
   * @brief 正态分布报警
   * @param  sample_test      My Param doc
   * @return StatAlarm
   */
  StatAlarm normal_dist_diff_alarm(SamplePoint sample_test);

  /**
   * @brief 劣化分析报警
   * @return StatAlarm
   */
  // StatAlarm degrad_diff_alarm_special();

  /**
   * @brief 回归分析报警
   * @param  sample_test      My Param doc
   * @return StatAlarm
   */
  StatAlarm regression_diff_alarm(SamplePoint sample_test);

  /**
   * @brief one class svm
   * @param  sample_test      My Param doc
   * @return StatAlarm
   */
  StatAlarm one_class_svm_diff_alarm(SamplePoint sample_test);

 protected:
  GbLogger gb_logger_;           ///< 全局logger
  const std::string rule_id_;    ///< 算法实例id
  const std::string rule_name_;  ///< 算法实例名称
  const size_t dims_;            ///< 数据的维度信息
  const TestMode test_mode_;     ///< 判断模式

  TimePoint tmp_store_time_;  ///< 临时的存储时间

  double p_ = 0.01;  ///< 系数
  double padding_low_, padding_up_;

  bool no_down_limit_ = false;  ///< 报警是否包含下限判断（适用于设定值-误差）

  bool is_first_save_ = true;    ///< 是否是本次程序启动第一次存储
  // bool is_data_loaded_ = false;  ///< 数据是否载入
  bool is_ready_to_detect_ = false;  ///< 程序本身是否准备探测数据合法性
  bool is_sampled_ = false;          ///< 是否已经拥有采样的数据

  std::chrono::system_clock::duration archive_interval_;  ///< 数据归档时间间隔

 protected:
  data_handler::Frame<data_handler::policy::ApproximateData>
      data_frame_;  ///< db2数据分布信息和本地数据特征信息
  distribution::Frame dist_frame_;      ///< 分布检测和置信区间框架
  regression::Frame regression_frame_;  ///< 回归性验证框架
  // oneClassSvm::Frame<2> oneClassSvm_frame_;  ///< one class svm验证框架

  small_vector<mix_cc::float_range_t, 3>
      legal_range_;  ///< 对于简单规则的合理区间
  // 统计参数
  std::vector<std::vector<double>> dist_param_;
};

}  // namespace stat_tools