| from typing import Tuple, Dict, Any | |
| import numpy as np | |
| from sklearn.cluster import KMeans, AgglomerativeClustering | |
| from sklearn.metrics import silhouette_score | |
| def run_kmeans(X: np.ndarray, k: int, random_state: int = 42) -> Dict[str, Any]: | |
| km = KMeans(n_clusters=k, random_state=random_state) | |
| labels = km.fit_predict(X) | |
| score = silhouette_score(X, labels) if k > 1 and len(X) > k else float("nan") | |
| return {"model": km, "labels": labels, "silhouette": score} | |
| def run_agglomerative(X: np.ndarray, k: int) -> Dict[str, Any]: | |
| agg = AgglomerativeClustering(n_clusters=k) | |
| labels = agg.fit_predict(X) | |
| score = silhouette_score(X, labels) if k > 1 and len(X) > k else float("nan") | |
| return {"model": agg, "labels": labels, "silhouette": score} | |
Xet Storage Details
- Size:
- 778 Bytes
- Xet hash:
- 04b92f0cccd4cef572fbba464c4a64d156df4421ebeb93eb249bd1e055b9691d
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.