python编写kmeans算法样例

以下是使用Python编写KMeans算法的样例：

import numpy as np
import matplotlib.pyplot as plt

def kmeans(X, K, max_iters=100):
    centroids = X[np.random.choice(len(X), K, replace=False)]
    for i in range(max_iters):
        clusters = [[] for _ in range(K)]
        for x in X:
            distances = [np.linalg.norm(x-c) for c in centroids]
            cluster_idx = np.argmin(distances)
            clusters[cluster_idx].append(x)
        prev_centroids = centroids
        centroids = [np.mean(cluster, axis=0) for cluster in clusters]
        if np.allclose(prev_centroids, centroids):
            break
    return centroids, clusters

# 生成数据
np.random.seed(0)
X = np.vstack([
    np.random.randn(100, 2) + np.array([2, 2]),
    np.random.randn(100, 2) + np.array([-2, -2]),
    np.random.randn(100, 2) + np.array([-2, 2])
])

# 运行KMeans算法
centroids, clusters = kmeans(X, 3)

# 可视化结果
colors = ['r', 'g', 'b']
for i, cluster in enumerate(clusters):
    for x in cluster:
        plt.scatter(x[0], x[1], c=colors[i])
for c in centroids:
    plt.scatter(c[0], c[1], marker='x', s=200, linewidths=3, color='k')
plt.show()

在这个样例中，我们使用Numpy和Matplotlib库来生成数据和可视化结果。在`kmeans()`函数中，我们首先随机初始化K个质心，然后迭代地将每个点分配到最近的质心，并根据分配结果重新计算质心位置。当质心位置不再变化时，算法停止迭代并返回质心和聚类结果。最后，我们使用Matplotlib库将数据点和质心可视化。