如何在python Scikit-learn中获得凝聚聚类"Centroid“

from sklearn.cluster import AgglomerativeClustering

model = AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='ward')

model.fit(X)
labels = model.labels_

centroids = []
for i in range(k):
    centroid = X[labels == i].mean(axis=0)
    centroids.append(centroid)

from sklearn.cluster import AgglomerativeClustering
import numpy as np

# 准备数据集
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])

# 创建AgglomerativeClustering对象
k = 2
model = AgglomerativeClustering(n_clusters=k, affinity='euclidean', linkage='ward')

# 拟合模型并进行预测
model.fit(X)
labels = model.labels_

# 计算质心
centroids = []
for i in range(k):
    centroid = X[labels == i].mean(axis=0)
    centroids.append(centroid)

print("聚类质心：")
for centroid in centroids:
    print(centroid)