给我写一个DBscan聚类代码

好的，以下是 Python 代码实现 DBSCAN 聚类算法：

from typing import List
import numpy as np

class DBSCAN:
    def __init__(self, eps: float, min_samples: int):
        self.eps = eps
        self.min_samples = min_samples

    def _get_neighbors(self, X: np.ndarray, p: int) -> List[int]:
        neighbors = []
        for i in range(X.shape[0]):
            if np.linalg.norm(X[p] - X[i]) < self.eps:
                neighbors.append(i)
        return neighbors

    def _expand_cluster(self, X: np.ndarray, labels: List[int], p: int, neighbors: List[int], cluster_id: int) -> bool:
        labels[p] = cluster_id
        i = 0
        while i < len(neighbors):
            n = neighbors[i]
            if labels[n] == -1:
                labels[n] = cluster_id
            elif labels[n] == 0:
                labels[n] = cluster_id
                n_neighbors = self._get_neighbors(X, n)
                if len(n_neighbors) >= self.min_samples:
                    neighbors += n_neighbors
            i += 1
        return True

    def fit(self, X: np.ndarray) -> List[int]:
        labels = np.zeros(X.shape[0], dtype=int)
        cluster_id = 0
        for i in range(X.shape[0]):
            if labels[i] != 0:
                continue
            neighbors = self._get_neighbors(X, i)
            if len(neighbors) < self.min_samples:
                labels[i] = -1
            else:
                cluster_id += 1
                self._expand_cluster(X, labels, i, neighbors, cluster_id)
        return list(labels)

使用方式：

import numpy as np
from sklearn.datasets import make_moons
import matplotlib.pyplot as plt

X, y = make_moons(n_samples=200, noise=0.1, random_state=42)
dbscan = DBSCAN(eps=0.3, min_samples=5)
labels = dbscan.fit(X)

plt.scatter(X[:, 0], X[:, 1], c=labels)
plt.show()

这是一个简单的示例，可以根据实际需求进行修改。