>>>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('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...
importnumpyasnpfromsklearn.metricsimportnormalized_mutual_info_score# 导入numpy进行数值运算,导入sklearn中的nmi函数 1. 2. 3. 2. 定义函数计算互信息 首先,我们定义一个函数来计算互信息。互信息是基于概率分布的度量。 defcompute_mutual_info(labels_true,labels_pred):"""计算互信息"""contingency_matrix=...
importnumpyasnpfromsklearn.metricsimportnormalized_mutual_info_score# 假设我们有两个聚类结果true_labels=np.array([1,1,0,0,1,0])predicted_labels=np.array([1,0,0,0,1,1])# 计算归一化互信息nmi=normalized_mutual_info_score(true_labels,predicted_labels)print(f"归一化互信息 (NMI):{nmi}") ...
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- 55 关注- 4 会员号:3720 +加关注 0 0 升级成为会员 «...
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...
互信息( Mutual Information)也是用来衡量两个数据分布的吻合程度。利用基于互信息的方法来衡量聚类效果需要实际类别信息,MI与NMI取值范围为[0,1],AMI取值范围为[-1,1],它们都是值越大意味看聚类结果与真实倩况越吻合。 代码 from sklearn.metrics.cluster import entropy, mutual_info_score, normalized_mutual_in...
normalized_mutual_info_score:sklearn.metrics.normalized_mutual_info_score(labels_true, labels_pred) v_measure_score:sklearn.metrics.v_measure_score(labels_true, labels_pred) 注:后续含labels_true参数的均需真实值参与 6、分类常用算法 Adaboost分类:class sklearn.ensemble.AdaBoostClassifier(base_estimato...
mutual_info = np.sum(mi[joint_prob > 0]) 归一化处理:将结果缩放到[0,1]区间 entropy_X = -np.sum(prob_X np.log2(prob_X + 1e-12)) entropy_Y = -np.sum(prob_Y np.log2(prob_Y + 1e-12)) normalized_mi = 2 mutual_info / (entropy_X + entropy_Y) 应用场景验证 独立变量测试...
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...