#include <iostream>
#include <fstream>
#include <vector>
#include <ceres/ceres.h>
#include <Eigen/Dense>
#include <matplotlibcpp.h>
#include <iomanip>
namespace plt = matplotlibcpp;

// 定义残差结构体
struct SineResidual {
    SineResidual(double x, double y) : x_(x), y_(y) {}
    
    template <typename T>
    bool operator()(const T* const params, T* residual) const {
        // params[0]: amplitude (a)
        // params[1]: frequency (b)
        // params[2]: phase (c)
        // params[3]: offset (d)
        residual[0] = y_ - (params[0] * sin(params[1] * x_ + params[2]) + params[3]);
        return true;
    }
    
private:
    const double x_;
    const double y_;
};

int main() {
    // 读取数据 实际
    std::ifstream file1("../f_data.csv");
    if (!file1.is_open()) {
        std::cerr << "Failed to open data file" << std::endl;
        return 1;
    }

    std::vector<double> x_data, y_actual, x_ideal,y_ideal;
    std::string line;
    
    // 跳过标题行
    std::getline(file1, line);
    
    while (std::getline(file1, line)) {
        double x, y;
        if (sscanf(line.c_str(), "%lf,%lf", &x, &y) == 2) {
            x_data.push_back(x);
            y_actual.push_back(y);
        }
    }
    file1.close();

    // 读取数据 理想
    std::ifstream file2("../f（1）_data.csv");
    if (!file2.is_open()) {
        std::cerr << "Failed to open data file" << std::endl;
        return 1;
    }

    // 跳过标题行
    std::getline(file2, line);

    while (std::getline(file2, line)) {
        double x, y;
        if (sscanf(line.c_str(), "%lf,%lf", &x, &y) == 2) {
            x_ideal.push_back(x);
            y_ideal.push_back(y);
        }
    }
    file2.close();

    // 初始参数猜测 [a, b, c, d]
    double params[2] = {5.0,1.0 };
    
    // 构建优化问题
    ceres::Problem problem;
    for (size_t i = 0; i < x_data.size(); ++i) {
        problem.AddResidualBlock( //向问题中添加误差项
        //使用自动求导，模板参数：误差类型、输出维度、输入维度
            new ceres::AutoDiffCostFunction<SineResidual, 1, 2>(
                new SineResidual(x_data[i], y_actual[i])),
            nullptr,
            params
        );
    }

    // 配置求解器
    ceres::Solver::Options options;
    //options.linear_solver_type = ceres::DENSE_QR;
    options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
    options.minimizer_progress_to_stdout = false;
    
    // 运行求解器
    ceres::Solver::Summary summary; //优化信息
    ceres::Solve(options, &problem, &summary); //开始优化
    
    // 输出结果
//    std::cout << summary.BriefReport() << "\n";
//    std::cout << "Final params: a=" << params[0] << " b=" << params[1]
//              << " c=" << params[2] << " d=" << params[3] << "\n";

    // 生成拟合曲线
    std::vector<double> y_fit;
    for (double x : x_data) {
        y_fit.push_back(params[0] * sin(params[1] * x + params[2]) + params[3]);
    }

//    // 生成理想曲线 (y = sin(x))
//    for (double x : x_data) {
//        y_ideal.push_back(sin(x));
//    }

    // 计算绝对差距和百分比差异
    double total_abs_diff = 0.0;
    double total_percent_diff = 0.0;
    int count = y_fit.size();

    for (size_t i = 0; i < count; ++i) {
        double abs_diff = std::abs(y_fit[i] - y_ideal[i]);
        double percent_diff = (abs_diff / std::abs(y_ideal[i])) * 100;

        // 处理y_ideal接近0时的特殊情况
        if (std::abs(y_ideal[i]) < 1e-6) {
            percent_diff = 0;  // 避免除以零
        }

        total_abs_diff += abs_diff;
        total_percent_diff += percent_diff;
    }

    double mean_abs_diff = total_abs_diff / count;
    double mean_percent_diff = total_percent_diff / count;

    // 将评估指标转换为字符串
    std::ostringstream stats_text;
    stats_text << "Mean Absolute Difference: " << std::fixed << std::setprecision(4) << mean_abs_diff << "\n"
               << "Mean Percentage Difference: " << std::fixed << std::setprecision(2) << mean_percent_diff << "%";

    // 可视化
    plt::figure_size(1200, 800);
    plt::scatter(x_data, y_actual, 10, {{"color", "red"}, {"label", "Actual Data"}});
    plt::plot(x_data, y_fit, {{"color", "green"}, {"label", "Fitted Curve"}});
    plt::plot(x_ideal, y_ideal, {{"color", "black"}, {"label", "Ideal Sine"}});

    // 在图像上添加评估指标文本
//    plt::text(0.02, 0.95, stats_text.str(),
//              {{"color", "blue"},
//               {"fontsize", "12"},
//               {"bbox", "dict(facecolor='white', alpha=0.7, edgecolor='none')"}});
    plt::text(0.01, -0.5, stats_text.str());
    plt::xlabel("x");
    plt::ylabel("y");
    plt::title("Sine Curve Fitting using Ceres");
    plt::legend();
    plt::save("sine_fit.png");
    plt::show();

    return 0;
}