写一个python的k-means算法

好的，我可以回答这个问题。K-means 是一种常见的聚类算法，可以用 Python 实现。以下是一个简单的 K-means 示例:

import numpy as np

def k_means(X, k, max_iters):
    """
    X: ndarray, shape=(m, n),
       m is the number of examples, n is the number of features
    k: int, the number of clusters
    max_iters: int, the maximum number of iterations
    """
    m, n = X.shape
    centroids = X[np.random.choice(m, k, replace=False), :]
    
    for _ in range(max_iters):
        # assignment step: assign each example to the cluster with the nearest centroid
        distances = np.sqrt(((X - centroids[:, np.newaxis])**2).sum(axis=2))
        labels = distances.argmin(axis=0)
        
        # update step: move the centroids to the mean of the examples assigned to them
        for j in range(k):
            centroids[j] = X[labels == j].mean(axis=0)
    
    return centroids, labels

以上代码是一个简单的 K-means 算法实现。其中，`X` 是数据集，`k` 是簇的数量，`max_iters` 是最大迭代次数。函数的输出是聚类后的簇中心和标签。