normalized_mutual_info_score(labels_true, labels_pred, *, average_method='arithmetic') 两个聚类之间的标准化互信息。 归一化互信息 (NMI) 是互信息 (MI) 分数的归一化,用于在 0(无互信息)和 1(完全相关)之间缩放结果。在此函数中,互信息通过 H(labels_true) 和H(labels_pred)) 的一些广义平均值...
# 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importadjusted_mutual_info_score[as 别名]defbench_k_means(estimator, name, data):estimator.fit(data)# A short explanation for every score:# homogeneity: each cluster contains only members of a single class (...
使用 sklearn 0.20.0,我将提供一个合成示例来重现该问题:metrics.normalized_mutual_info_score([0]*100001, [0]*100000 + [1])metrics.normalized_mutual_info_score([0]*110001, [0]*110000 + [1])我希望下面的答案是 0,但我分别得到了 7.999 和 -7.999。 查看完整描述1 回答江户川乱折腾 TA贡献...
1.输出 System.out.println(); //输出且换行 System.out.print(); //输出且不换行 System.out...
Torchmetrics - Machine learning metrics for distributed, scalable PyTorch applications. - torchmetrics/src/torchmetrics/clustering/mutual_info_score.py at v1.3.2 · Lightning-AI/torchmetrics
Policies Must Score a Mutual LikeRead the full-text online article and more details about "Policies Must Score a Mutual Like" by Gale, Sarah Fister - Workforce Management, Vol. 91, Issue 8, August 2012Gale, Sarah Fister...
adjusted_mutual_info_score(gtlabels[:numeval], labels[:numeval]) nmi = metrics.normalized_mutual_info_score(gtlabels[:numeval], labels[:numeval]) acc = clustering_accuracy(gtlabels[:numeval], labels[:numeval]) return ari, ami, nmi, acc ...
t =normalized_mutual_info_score(mlp.predict(X[te]), y[te]) print("Fold training accuracy: %f"% t) total += t this_score = []foriinmlp.oo_score: this_score.append(normalized_mutual_info_score(i, y[te])) oo_score_bag.append(this_score)frommatplotlibimportpyplotasplt ...
mi =mutual_info_score(None,None, contingency=c_xy)returnmi 开发者ID:bbfrederick,项目名称:rapidtide,代码行数:6,代码来源:correlate.py 示例4: test_mutual_info_score ▲点赞 5▼ # 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importmutual_info_score[as 别...
1.0 >>> adjusted_mutual_info_score([0, 0, 1, 1], [1, 1, 0, 0]) ... 1.0 如果类成员完全分散在不同的集群中,则分配完全是in-complete,因此 AMI 为空: >>> adjusted_mutual_info_score([0, 0, 0, 0], [0, 1, 2, 3]) ... 0.0 相关用法 Python sklearn adjusted_rand_score...