互信息(Mutual Information)的计算 互信息(Mutual Information)的计算我们以sklearn几个函数做简单示例一下。 mutual_info_score函数用于计算两个离散随机变量之间的互信息。它可以用于衡量两个离散随机变量之间的相关性或依赖性,其值的范围通常在0到正无穷大之间。在实际应用中,它常用于特征选择、聚类评估和分类模型的...
简介 互信息(Mutual Information)是信息论中的概念,用来衡量两个随机变量之间的相互依赖程度。对于Mutual Information的介绍会用到KL散度(Kullback–Leibler divergence)的知识,可以参考https://www.jianshu.com/p/00254c4d0931。 定义 互信息可以定义为两个随机变量的联合分布与边缘分布的乘积的KL散度。 根据上述定义,...
plt.title("Mutual Information Scores") plt.figure(dpi=100, figsize=(8, 5)) plot_mi_scores(mi_scores) 我们发现curb_weight的Mi Scores很高,因此,我们对curb_weight和price做图: 作图代码: sns.relplot(x="curb_weight", y="price", data=df); fuel_type的Mi Score很低,但通过做交互效应图后,发现...
# 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]defadjusted_mutual_information(x, tx, y, ty, ffactor=3, maxdev=3):x, y = discretized_sequences(x, tx, y, ty, ffactor, maxdev)try:returnadjusted_mutual_info_...
img_cp1= np.reshape(img_cp1, -1) img_cp2= np.reshape(img_cp2, -1)print(img_cp2.shape)print(img_cp1.shape) mutual_infor=mr.mutual_info_score(img_cp1, img_cp2)print(mutual_infor)
(0,2,3,1)#就是变成 N*N 的形式,对角线为正例对# Since we have a big tensor with both positive and negative samples, we need to mask.mask=torch.eye(N).to(l.device)#形成正例对损失的 mask,为对角线n_mask=1-mask#剩下都是负例对# Compute the positive and negative score. Average ...
The performance of clustering is analyzed with score, Rand index, adjusted Rand index, mutual information score (MIS), normalized MIS, adjusted MIS, homogeneity score, completeness score, and VMeasure. The scripting is written in Python and implemented with Spyder in Anaconda Navigator IDE, and ...
python normalized mutual information 深入理解Python中的归一化互信息 在数据科学和机器学习的领域,评估和比较不同分组或聚类的质量至关重要。其中,互信息(Mutual Information, MI)是一种衡量两个变量之间依赖关系的指标。为了更好地比较不同大小的数据集,通常使用归一化互信息(Normalized Mutual Information, NMI)。
How to calculate mutual information in PyTorch (differentiable estimator) Torchmetrics has MutualInfoScore. Example from their docs: >>> import torch >>> from torchmetrics.clustering import MutualInfoScore >>> preds = torch.tensor([2, 1, 0, 1, 0]) ... ...
" ([Mutual Information](https://www.kaggle.com/code/ryanholbrook/mutual-information)) >[!card] The higher the mutual information score, the more a feature reduces uncertainty about the target >"The least possible mutual information between quantities is 0.0. When MI is zero, the quantities ...