给出KANN-DBSCAN的python代码示例

以下是KANN-DBSCAN的Python代码示例：

import numpy as np
from sklearn.neighbors import NearestNeighbors

class KANN_DBSCAN():
    def __init__(self, eps=0.5, min_samples=5):
        self.eps = eps
        self.min_samples = min_samples

    def fit(self, X):
        self.N = len(X) # 数据点的数量
        self.visited = np.zeros(self.N, dtype=bool) # 是否已经被访问过
        self.labels = np.zeros(self.N, dtype=int) # 每个点的标签（即类别）
        self.distances = NearestNeighbors().fit(X).kneighbors(X)[0] # 每个点到其它所有点的距离
        self.c = 0 # 当前类别编号

        for i in range(self.N):
            if not self.visited[i]:
                self.visited[i] = True
                neighbors = self._region_query(i)
                if len(neighbors) < self.min_samples:
                    self.labels[i] = -1 # 噪声点
                else:
                    self._expand_cluster(i, neighbors)

        return self.labels

    def _region_query(self, i):
        # 返回 i 的邻居（即到 i 距离小于 eps 的所有点的下标）
        return np.where(self.distances[i] <= self.eps)[0]

    def _expand_cluster(self, i, neighbors):
        self.c += 1
        self.labels[i] = self.c
        j = 0
        while j < len(neighbors):
            k = neighbors[j]
            if not self.visited[k]:
                self.visited[k] = True
                neighbors_k = self._region_query(k)
                if len(neighbors_k) >= self.min_samples:
                    neighbors = np.concatenate([neighbors, neighbors_k])
            if self.labels[k] == 0:
                self.labels[k] = self.c
            j += 1

希望这个代码示例对你有所帮助！