用c++写一个DBSCAN聚类算法

好的，以下是一个简单的C++实现DBSCAN聚类算法的示例代码：

#include <iostream>
#include <vector>
#include <cmath>

using namespace std;

struct Point {
    double x, y;
    bool visited;
    int cluster;
};

double distance(Point a, Point b) {
    return sqrt(pow(a.x - b.x, 2) + pow(a.y - b.y, 2));
}

vector<Point> regionQuery(vector<Point>& points, int index, double eps) {
    vector<Point> neighbors;
    for (int i = 0; i < points.size(); i++) {
        if (distance(points[i], points[index]) <= eps) {
            neighbors.push_back(points[i]);
        }
    }
    return neighbors;
}

void expandCluster(vector<Point>& points, int index, vector<Point>& neighbors, int cluster, double eps, int minPts) {
    points[index].cluster = cluster;
    for (int i = 0; i < neighbors.size(); i++) {
        int neighborIndex = &neighbors[i] - &points[0];
        if (!neighbors[i].visited) {
            neighbors[i].visited = true;
            vector<Point> newNeighbors = regionQuery(points, neighborIndex, eps);
            if (newNeighbors.size() >= minPts) {
                neighbors.insert(neighbors.end(), newNeighbors.begin(), newNeighbors.end());
            }
        }
        if (points[neighborIndex].cluster == -1) {
            points[neighborIndex].cluster = cluster;
        }
    }
}

void DBSCAN(vector<Point>& points, double eps, int minPts) {
    int cluster = 0;
    for (int i = 0; i < points.size(); i++) {
        if (!points[i].visited) {
            points[i].visited = true;
            vector<Point> neighbors = regionQuery(points, i, eps);
            if (neighbors.size() < minPts) {
                points[i].cluster = -1;
            } else {
                expandCluster(points, i, neighbors, cluster, eps, minPts);
                cluster++;
            }
        }
    }
}

int main() {
    vector<Point> points = {{1, 1}, {1, 2}, {2, 1}, {10, 10}, {10, 12}, {12, 10}, {50, 50}, {50, 52}, {52, 50}};
    double eps = 3;
    int minPts = 3;
    DBSCAN(points, eps, minPts);
    for (int i = 0; i < points.size(); i++) {
        cout << "(" << points[i].x << ", " << points[i].y << ") - Cluster " << points[i].cluster << endl;
    }
    return 0;
}

这个示例代码使用了一个Point结构体来存储点的坐标、是否被访问过、聚类编号等信息。`distance()`函数用来计算两个点之间的距离，`regionQuery()`函数用来查询某个点的邻居点，`expandCluster()`函数用来将某个点及其邻居点分配到同一个聚类中。最后的`DBSCAN()`函数是整个算法的主体部分，通过遍历所有点来实现聚类。在这个示例代码中，我们使用了一个简单的点集来测试算法的正确性，并输出了每个点所属的聚类编号。