我需要实现Kmeans聚类算法实现对一对点的聚类请给我python代码

以下是K-means聚类算法的Python实现：

import numpy as np

def distance(point1, point2):
    return np.sqrt(np.sum((point1 - point2) ** 2))

def kmeans(points, k):
    n = points.shape[0]
    centroids = points[np.random.choice(n, k, replace=False)]
    clusters = [[] for _ in range(k)]
    while True:
        for point_idx, point in enumerate(points):
            min_dist = np.inf
            closest_centroid_idx = None
            for centroid_idx, centroid in enumerate(centroids):
                dist = distance(point, centroid)
                if dist < min_dist:
                    min_dist = dist
                    closest_centroid_idx = centroid_idx
            clusters[closest_centroid_idx].append(point_idx)

        new_centroids = np.zeros((k, points.shape[1]))
        for cluster_idx, cluster in enumerate(clusters):
            cluster_mean = np.mean(points[cluster], axis=0)
            new_centroids[cluster_idx] = cluster_mean

        if np.allclose(new_centroids, centroids):
            break

        centroids = new_centroids
        clusters = [[] for _ in range(k)]

    return clusters

其中，distance函数用于计算两个点之间的欧几里得距离，kmeans函数实现了K-means聚类算法。它接受一个二维数组points，其中每一行表示一个点的坐标，以及一个整数k，表示需要将points分成k个簇。函数返回一个包含k个列表的列表clusters，每个列表表示一个簇，包含该簇中点的索引。