写一个不直接调用DBSCAN的包便将已有的xyz点云数据进行DBSCAN分成四类的Python代码

以下是一个简单的实现DBSCAN聚类算法的Python代码：

import numpy as np

class DBSCAN:
    def __init__(self, eps=0.5, min_samples=5):
        self.eps = eps
        self.min_samples = min_samples
        
    def fit(self, X):
        self.X = X
        self.labels = np.zeros(len(X))
        self.cluster = 0
        for i in range(len(X)):
            if self.labels[i] != 0:
                continue
            neighbors = self.region_query(i)
            if len(neighbors) < self.min_samples:
                self.labels[i] = -1
            else:
                self.cluster += 1
                self.grow_cluster(i, neighbors, self.cluster)

    def region_query(self, i):
        neighbors = []
        for j in range(len(self.X)):
            if np.linalg.norm(self.X[i] - self.X[j]) < self.eps:
                neighbors.append(j)
        return neighbors
    
    def grow_cluster(self, i, neighbors, cluster):
        self.labels[i] = cluster
        for j in neighbors:
            if self.labels[j] == 0:
                new_neighbors = self.region_query(j)
                if len(new_neighbors) >= self.min_samples:
                    neighbors += new_neighbors
            if self.labels[j] != 0:
                continue
            self.labels[j] = cluster

其中，`eps`和`min_samples`是DBSCAN算法的两个重要参数。`eps`表示邻域半径，`min_samples`表示最小样本数。在`fit`函数中，对每个点进行遍历，如果该点已被标记，则跳过；否则，求出该点的邻居集合，如果邻居数小于`min_samples`，则将该点标记为噪声点；否则，为该点分配一个新的簇号，并根据邻居集合逐步扩展簇。最终，返回聚类后的结果，即每个点对应的簇号。