我正在使用 scipy.cluster 进行层次聚类，然后在不同的截止值下使用 fcluster。我也想使用 scikit 的剪影分数。我看到帖子如何使用 scikit-learn 剪影分数计算 scipy 的 fcluster 的剪影分数？ 但是，我收到错误“布尔索引太多”？？
我的代码如下：
import fastcluster
from sklearn import metrics
from scipy.cluster import hierarchy as hac
Temps=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
Distance=[]
#read the Distance obtained as a list then
Distances=np.array(Distances)
Z=fastcluster.linkage(Distances, "complete", "euclidean")
for Cutoff in Temps:
results=hac.fcluster(Z,Cutoff,'distance')
metrics.silhouette_score(Distances, results, metric="euclidean")
错误报告是：
Traceback (most recent call last):
File "Clustering_2.py", line 93, in <module>
main(argv)
File "Clustering_2.py", line 69, in main
silscore=metrics.silhouette_score(Distances, results,metric='euclidean')
File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 93, in silhouette_score
return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 157, in silhouette_samples
for i in range(n)])
File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 187, in _intra_cluster_distance
a = np.mean(distances_row[mask])
ValueError: too many boolean indices
有什么问题？请指教。谢谢