如何在python中对极坐标系数据进行分组或聚类

from sklearn.cluster import KMeans
import numpy as np

# 极坐标系数据
polar_data = [(1, 0.5), (2, 0.8), (3, 1.2), (4, 1.5), (5, 1.7), (6, 2.0)]

# 转换为笛卡尔坐标系数据
cartesian_data = [(r * np.cos(theta), r * np.sin(theta)) for r, theta in polar_data]

X = np.array(cartesian_data)

# 创建KMeans对象，指定簇的数量
kmeans = KMeans(n_clusters=2)

# 对数据进行聚类
kmeans.fit(X)

# 获取每个数据点所属的簇
labels = kmeans.labels_

# 获取聚类中心点的坐标
centers = kmeans.cluster_centers_

import matplotlib.pyplot as plt

# 绘制原始数据点
plt.scatter(X[:, 0], X[:, 1], c=labels)
# 绘制聚类中心点
plt.scatter(centers[:, 0], centers[:, 1], marker='x', color='red')
plt.show()