找dbscan中最优参数的代码
时间: 2023-06-03 12:04:37 浏览: 47
Sure, here is an example code for finding the optimal parameters for DBSCAN algorithm:
```
from sklearn.cluster import DBSCAN
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
import numpy as np
# Generate data
X, y = make_blobs(n_samples=1000, centers=3, n_features=10, random_state=42)
# Scale the data
X = StandardScaler().fit_transform(X)
# Find optimal parameters
eps_values = np.linspace(0.5, 5, 100)
min_samples_values = range(1, 10)
best_score = -1
best_params = {}
for eps in eps_values:
for min_samples in min_samples_values:
dbscan = DBSCAN(eps=eps, min_samples=min_samples)
dbscan.fit(X)
score = len(set(dbscan.labels_)) - (1 if -1 in dbscan.labels_ else 0)
if score > best_score:
best_score = score
best_params = {'eps': eps, 'min_samples': min_samples}
print('Best parameters:', best_params)
```
Hope you find it helpful!