python手动实现DBSCAN

DBSCAN是一种基于密度的聚类算法。下面是手动实现DBSCAN的Python代码示例：

import numpy as np

def dbscan(data, eps, min_samples):
    n_samples, n_features = data.shape
    visited = np.zeros(n_samples, dtype=bool)
    labels = np.zeros(n_samples, dtype=int)
    label = 0
    
    for i in range(n_samples):
        if visited[i]:
            continue
        visited[i] = True
        
        neighb = get_neighbors(data, i, eps)
        if len(neighb) < min_samples:
            labels[i] = -1
        else:
            label += 1
            labels[i] = label
            expand_cluster(data, visited, labels, i, neighb, label, eps, min_samples)
            
    return labels
            
def expand_cluster(data, visited, labels, i, neighb, label, eps, min_samples):
    for j in neighb:
        if not visited[j]:
            visited[j] = True
            new_neighb = get_neighbors(data, j, eps)
            if len(new_neighb) >= min_samples:
                neighb = neighb + new_neighb
        if labels[j] == 0:
            labels[j] = label

def get_neighbors(data, i, eps):
    neighb = []
    for j in range(len(data)):
        if np.linalg.norm(data[i]-data[j]) < eps:
            neighb.append(j)
    return neighb

其中，data是待聚类的数据，eps为邻域半径，min_samples为最小样本数。函数dbscan返回聚类结果，labels是每个样本的类别标记，-1表示噪声点。

需要注意的是，这个手动实现的DBSCAN算法可能会比较慢，当数据量较大时，可以考虑使用sklearn库中的DBSCAN算法。