k_means++不调用sklearn的python代码

好的，下面是一个示例的 K-Means 算法的 Python 代码，不需要使用 sklearn：

import numpy as np
import random

def k_means(data, k, max_iter=100):
    # 初始化质心
    centroids = random.sample(list(data), k)
    
    for i in range(max_iter):
        # 分配数据点到最近的质心
        clusters = [[] for _ in range(k)]
        for point in data:
            distances = [np.linalg.norm(point - c) for c in centroids]
            cluster_idx = np.argmin(distances)
            clusters[cluster_idx].append(point)
        
        # 更新质心
        new_centroids = []
        for cluster in clusters:
            if len(cluster) > 0:
                new_centroids.append(np.mean(cluster, axis=0))
            else:
                new_centroids.append(random.choice(list(data)))
        centroids = new_centroids
    
    return centroids

希望能够帮到你！