代码

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#include<iostream>
#include<vector>
#include <memory>
#include <cmath>
#include <random>
#include <algorithm>
#include <chrono>

class Cluster
{
public:
    struct Options
    {
        struct Threshold
        {
            double distance = 2.0;
            double theta = 10.0;
        };
        Threshold threshold = {};
    };
    struct Pose
    {
        double x = 0.0;
        double y = 0.0;
        double theta = 0.0;
    };
public:
    Options m_options;
     
public :
    Cluster(const Options &options, std::vector<Pose> &pose_list);
    ~Cluster();
    void Run();
    void print_cluster();
protected:
    std::vector<Pose> m_pose_list;
    // 数据矩阵保存聚类结果(pose, cluster_id)
    std::vector<std::pair<Pose, uint>> m_cluster_result;
};

Cluster::Cluster(const Options &options, std::vector<Pose> &pose_list)
    : m_options(options)
    , m_pose_list(pose_list)
{
    Run();
}

Cluster::~Cluster()
{
}

void Cluster::Run()
{
    // 初始化聚类结果
    m_cluster_result.resize(m_pose_list.size());
    for (uint i = 0; i < m_pose_list.size(); ++i)
    {
        m_cluster_result[i].first = m_pose_list[i];
        m_cluster_result[i].second = i;
    }

    // 初始化聚类结果
    std::vector<uint> cluster_id_list(m_pose_list.size(), 0);
    uint cluster_id = 0;
    for (uint i = 0; i < m_pose_list.size(); ++i)
    {
        if (cluster_id_list[i] == 0)
        {
            cluster_id_list[i] = cluster_id;
            cluster_id++;
            for (uint j = i + 1; j < m_pose_list.size(); ++j)
            {
                if (cluster_id_list[j] == 0)
                {
                    double distance = std::hypot(m_pose_list[i].x - m_pose_list[j].x, m_pose_list[i].y - m_pose_list[j].y);
                    double theta = std::abs(m_pose_list[i].theta - m_pose_list[j].theta);
                    if (distance < m_options.threshold.distance && theta < m_options.threshold.theta)
                    {
                        cluster_id_list[j] = cluster_id_list[i];
                    }
                }
            }
        }
    }
    // 检查cluster_id_list是否存在阈值交集,并合并交集
    for (uint i = 0; i < m_pose_list.size(); ++i)
    {
        for(uint j = i + 1; j < m_pose_list.size(); ++j)
        {
            double distance = std::hypot(m_pose_list[i].x - m_pose_list[j].x, m_pose_list[i].y - m_pose_list[j].y);
            double theta = std::abs(m_pose_list[i].theta - m_pose_list[j].theta);
            if (distance < m_options.threshold.distance && theta < m_options.threshold.theta)
            {
                if (cluster_id_list[i] != cluster_id_list[j])
                {
                    for (uint k = 0; k < m_pose_list.size(); ++k)
                    {
                        if (cluster_id_list[k] == cluster_id_list[j])
                        {
                            cluster_id_list[k] = cluster_id_list[i];
                        }
                    }
                }
            }
        }
    }

    // 更新聚类结果
    for (uint i = 0; i < m_pose_list.size(); ++i)
    {
        m_cluster_result[i].second = cluster_id_list[i];
    }
    // 按照聚类结果排序
    // std::sort(m_cluster_result.begin(), m_cluster_result.end(), [](const std::pair<Pose, uint> &a, const std::pair<Pose, uint> &b) {
    //     return a.second < b.second;
    // });
    
    // 打印
    print_cluster();
}

void Cluster::print_cluster()
{
    std::cout << "cluster result: " << std::endl;
    for (uint i = 0; i < m_cluster_result.size(); ++i)
    {
        std::cout << "pose: " << m_cluster_result[i].first.x << " " << m_cluster_result[i].first.y << " " << m_cluster_result[i].first.theta << " " << "cluster_id: " << m_cluster_result[i].second << std::endl;
    }
    // 判断是否聚类
    bool is_cluster = true;
    for (auto cluster_id : m_cluster_result)
    {
        if (cluster_id.second != m_cluster_result.front().second)
        {
            is_cluster = false;
            break;
        }
    }
    std::cout << "is_cluster: " << is_cluster << std::endl;
}

int main()
{
    std::vector<Cluster::Pose> pose_list;
    // 随机生成一组pose_list
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<> dis(0, 10);
    for (int i = 0; i < 10; i++)
    {
        Cluster::Pose pose;
        pose = {dis(gen), dis(gen), dis(gen)};
        pose_list.push_back(pose);
    }
    Cluster::Options options;
    options.threshold.distance = 5;
    options.threshold.theta = 50;

    Cluster cluster(options, pose_list);


    return 0;
}