sklearn中DBI计算代码
时间: 2024-04-24 16:24:15 浏览: 157
在scikit-learn库(sklearn)中,没有内置的Davies-Bouldin Index(DBI)计算函数。但是,您可以使用一些NumPy和scipy库的函数来实现它。
以下是一个示例代码,它使用sklearn的KMeans聚类算法来计算DBI:
```python
import numpy as np
from sklearn.metrics import pairwise_distances
from sklearn.cluster import KMeans
def davies_bouldin_index(X, labels):
k = len(np.unique(labels))
centroids = np.zeros((k, X.shape[1]))
cluster_sizes = np.bincount(labels)
for i in range(k):
centroids[i] = np.mean(X[labels == i], axis=0)
distances = pairwise_distances(centroids, X)
intra_cluster_distances = np.zeros(k)
for i in range(k):
cluster_points = X[labels == i]
intra_cluster_distances[i] = np.mean(pairwise_distances(cluster_points, [centroids[i]]))
inter_cluster_distances = np.zeros((k, k))
for i in range(k):
for j in range(k):
if i != j:
inter_cluster_distances[i][j] = (intra_cluster_distances[i] + intra_cluster_distances[j]) / distances[i][j]
dbi = np.max(inter_cluster_distances.sum(axis=1) / cluster_sizes)
return dbi
# 示例用法
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
labels = kmeans.labels_
dbi = davies_bouldin_index(X, labels)
print("DBI:", dbi)
```
请注意,此示例代码仅用于说明如何计算DBI指数。实际上,您可能需要根据您的需求进行调整和修改。