print(NMI(A,B)) print(metrics.normalized_mutual_info_score(A,B)) # 直接调用sklearn中的函数 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 运...
print('Mutual Information Based Scores for K-Means is:', metrics.normalized_mutual_info_score(df['cluster_id'], km_labels)) print('Mutual Information Based Scores for Affinity Propagation is:', metrics.normalized_mutual_info_score(df['cluster_id'], af_labels)) print('Mutual Information Based...
>>>from sklearnimportmetrics>>>labels_true=[0,0,0,1,1,1]>>>labels_pred=[0,0,1,1,2,2]>>>metrics.adjusted_rand_score(labels_true,labels_pred)0.24 . 1.2 Mutual Information based scores 互信息 Two different normalized versions of this measure are available, Normalized Mutual Information(N...
print(metrics.normalized_mutual_info_score(A,B)) # 直接调用sklearn中的函数 运行结果: 0.3645617718571898 0.3646247961942429 分类: Machine Learning and Optimization 标签: NMI , Python 好文要顶 关注我 收藏该文 微信分享 Picassooo 粉丝- 53 关注- 4 会员号:3720 +加关注 0 0 升级成为会员 «...
print(metrics.normalized_mutual_info_score(A,B)) # 直接调用sklearn中的函数 运行结果: 0.3645617718571898 0.3646247961942429 分类: Machine Learning and Optimization 标签: NMI , Python 好文要顶 关注我 收藏该文 微信分享 Picassooo 粉丝- 53 关注- 4 会员号:3720 +加关注 0 0 升级成为会员 «...
normalized_mutual_info_score:NMI(U,V)=MI(U,V)H(U)H(V)√ N M I ( U , V ) = M I ( U , V ) H ( U ) H ( V ) U 的熵:H(U)=−∑|U|i=1P(i)log(P(i)) H ( U ) = − ∑ i = 1 | U | P ( i ) l o g ( P ( i ) ) ...
normalized_mutual_info_score( labels_true, labels_pred, *, average_method='arithmetic', ) 6.2.2 示例 代码语言:javascript 复制 normalized_mutual_info_score([0, 0, 1, 1], [1, 1, 0, 0]) 「输出」: 代码语言:javascript 复制 1.0 6.3 Jaccard系数 代码语言:javascript 复制 from sklearn....
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score, adjusted_rand_score, normalized_mutual_info_scorefrom sklearn.datasets import make_blobs 生成模拟数据 X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) 使用K-means...
predict(x) 7 nmi = normalized_mutual_info_score(y, y_pred) 8 print("NMI: ", nmi)# 0.758 在上述代码中,第1行用来导入sklearn中的KMeans聚类模型;第2行用来导入聚类评估指标,其范围为0到1越大表示结果越好,这部分内容将在下一篇文章中进行介绍;第4行代码则是用来初始化KMeans模型,参数n_clusters...
'neg_brier_score', 'adjusted_rand_score', 'rand_score', 'homogeneity_score', 'completeness_score', 'v_measure_score', 'mutual_info_score', 'adjusted_mutual_info_score', 'normalized_mutual_info_score', 'fowlkes_mallows_score', 'precision', 'precision_macro', 'precision_micro', 'precision...