列出上述评价指标的python调库实现

下面是四个评价指标的Python调库实现：

1. Silhouette Score

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# X为样本特征矩阵，y_pred为聚类结果
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
y_pred = kmeans.labels_

# 计算Silhouette Score
score = silhouette_score(X, y_pred, metric='euclidean')

2. Calinski-Harabasz Index

from sklearn.cluster import KMeans
from sklearn.metrics import calinski_harabasz_score

# X为样本特征矩阵，y_pred为聚类结果
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
y_pred = kmeans.labels_

# 计算Calinski-Harabasz Index
score = calinski_harabasz_score(X, y_pred)

3. Davies-Bouldin Index

from sklearn.cluster import KMeans
from sklearn.metrics import davies_bouldin_score

# X为样本特征矩阵，y_pred为聚类结果
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
y_pred = kmeans.labels_

# 计算Davies-Bouldin Index
score = davies_bouldin_score(X, y_pred)

4. Normalized Mutual Information

from sklearn.cluster import KMeans
from sklearn.metrics import normalized_mutual_info_score

# X为样本特征矩阵，y_true为真实标签，y_pred为聚类结果
kmeans = KMeans(n_clusters=3, random_state=0).fit(X)
y_pred = kmeans.labels_

# 计算Normalized Mutual Information
score = normalized_mutual_info_score(y_true, y_pred)

在上面的示例代码中，我们使用了sklearn库中的KMeans聚类模型，以及metrics模块中的评价指标函数。需要注意的是，不同的评价指标函数可能需要不同的参数，具体可以查看文档或者函数说明。