点云聚类算法C语言代码

点云聚类是一种数据处理技术，常用于无监督学习中，将一组相似的三维坐标点归为一类。在C语言中实现点云聚类通常会用到一些常见的数据结构和算法，比如K-means、DBSCAN等。以下是一个简单的K-means聚类算法的示例：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define MAX_POINTS 100
#define K_CLUSTERS 3

typedef struct {
    float x, y, z;
} Point;

void initialize_clusters(Point* points, int k) {
    // 随机选择初始质心
    for (int i = 0; i < k; i++) {
        int random_index = rand() % MAX_POINTS;
        Point* current = &points[random_index];
        // 初始化质心位置
        points[i].x = points[random_index].x;
        points[i].y = points[random_index].y;
        points[i].z = points[random_index].z;
    }
}

void assign_points_to_clusters(Point* points, int k, float distance_threshold) {
    // 指定每个点属于哪个簇
    for (int i = 0; i < MAX_POINTS; i++) {
        int closest_cluster = -1;
        float min_distance = INFINITY;
        for (int j = 0; j < k; j++) {
            float dist = sqrt(pow(points[j].x - points[i].x, 2)
                             + pow(points[j].y - points[i].y, 2)
                             + pow(points[j].z - points[i].z, 2));
            if (dist < min_distance && dist <= distance_threshold) {
                min_distance = dist;
                closest_cluster = j;
            }
        }
        points[i].cluster_id = closest_cluster;
    }
}

void update_clusters(Point* points, int k) {
    for (int i = 0; i < k; i++) {
        Point cluster_center;
        cluster_center.x = cluster_center.y = cluster_center.z = 0;
        
        for (int j = 0; j < MAX_POINTS; j++) {
            if (points[j].cluster_id == i) {
                cluster_center.x += points[j].x;
                cluster_center.y += points[j].y;
                cluster_center.z += points[j].z;
            }
        }
        cluster_center.x /= MAX_POINTS;
        cluster_center.y /= MAX_POINTS;
        cluster_center.z /= MAX_POINTS;
        
        points[i] = cluster_center;
    }
}

// 主函数
int main() {
    // 初始化点云数据...
    // 调用上述函数进行聚类...

    return 0;
}

请注意，这只是一个简化版的K-means算法示例，并未包含所有细节，如迭代过程和停止条件。实际应用中，还需要考虑如何结束循环、设置合适的迭代次数和收敛阈值等问题。