提供一个三支K-means算法代码python

以下是一个简单的三支K-means算法的Python代码：

import numpy as np

def kmeans(X, K, max_iters):
    centroids = X[np.random.choice(X.shape[0], K, replace=False), :]
    for i in range(max_iters):
        # Calculate the distance between each point and each centroid
        distances = np.sqrt(((X - centroids[:, np.newaxis])**2).sum(axis=2))
        # Assign each point to its nearest centroid
        labels = np.argmin(distances, axis=0)
        # Update the centroids based on the points assigned to them
        for j in range(K):
            centroids[j] = np.mean(X[labels == j], axis=0)
    return centroids, labels

# Example usage
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
K = 2
max_iters = 10
centroids, labels = kmeans(X, K, max_iters)
print("Final centroids:", centroids)
print("Final labels:", labels)

该代码使用了Numpy库来计算距离和均值，以及随机选择初始质心。在每次迭代中，它计算每个点到每个质心的距离，并将每个点分配给最近的质心。然后，它根据分配给它们的点更新每个质心的位置。该算法迭代完成后返回最终质心和标签。