重构python中的K-means算法，使用numpy

import numpy as np
import random

def k_means(data, k, max_iterations):
    # 随机选择k个初始聚类中心
    centroids = random.sample(list(data), k)
    
    for _ in range(max_iterations):
        # 初始化聚类结果
        clusters = [[] for _ in range(k)]
        
        # 将每个数据点分配到最近的聚类中心
        for point in data:
            distances = [np.linalg.norm(point - centroid) for centroid in centroids]
            cluster_index = np.argmin(distances)
            clusters[cluster_index].append(point)
        
        # 更新聚类中心为每个聚类的平均值
        for i in range(k):
            centroids[i] = np.mean(clusters[i], axis=0)
    
    return clusters, centroids

data = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
k = 2
max_iterations = 10

clusters, centroids = k_means(data, k, max_iterations)
print("聚类结果：")
for i, cluster in enumerate(clusters):
    print(f"聚类{i+1}: {cluster}")
print("聚类中心：")
for i, centroid in enumerate(centroids):
    print(f"聚类中心{i+1}: {centroid}")

聚类结果：
聚类1: [array([1, 2]), array([1, 4]), array([1, 0])]
聚类2: [array([4, 2]), array([4, 4]), array([4, 0])]
聚类中心：
聚类中心1: [1. 2.]
聚类中心2: [4. 2.]