我想要python实现的k-means算法

A：

下面是Python实现的k-means算法的示例代码：

import numpy as np

class KMeans:
    def __init__(self, k, max_iters=100):
        self.k = k
        self.max_iters = max_iters

    def fit(self, X):
        m, n = X.shape

        # 初始化聚类中心
        centroid_idx = np.random.choice(m, size=self.k, replace=False)
        centroids = X[centroid_idx]

        for i in range(self.max_iters):
            # 计算每个点到聚类中心的距离
            distances = np.linalg.norm(X[:, np.newaxis, :] - centroids, axis=-1)

            # 分配每个点到最近的聚类中心
            labels = np.argmin(distances, axis=1)

            # 计算新的聚类中心
            for j in range(self.k):
                mask = labels == j
                if np.any(mask):
                    centroids[j] = X[mask].mean(axis=0)

        self.centroids = centroids
        self.labels = labels

    def predict(self, X):
        distances = np.linalg.norm(X[:, np.newaxis, :] - self.centroids, axis=-1)
        return np.argmin(distances, axis=1)

使用方法：

from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# 生成测试数据
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.6)

# 初始化k-means模型
kmeans = KMeans(k=4)

# 训练模型
kmeans.fit(X)

# 预测聚类标签
labels = kmeans.predict(X)

# 绘制聚类结果
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.scatter(kmeans.centroids[:, 0], kmeans.centroids[:, 1], c='black', s=200, alpha=0.5)
plt.show()

输出结果：