使用 RMSE 计算相似度

Question

我有以下数据：

物体	l2a	l2b	l4	l5
a	0.6649	0.5916	0.033569	0.557373
b	0.8421	0.5132	0.000000	0.697193
c	0.6140	0.2807	0.084217	0.650313
d	0.7619	0.3810	0.000000	0.662306
e	0.6957	0.3043	0.000000	0.645135

是否可以使用 RMSE 来测量 (a-b)、(a-c)、(a-d)、(a-e)、(b-c)、...、(d,e) 之间的相似度？

例如：

对象a（_a）和对象b（_b）之间的相似性：

diff_l2a = l2a_a - l2a_b

diff_l2b = l2b_a - l2b_b

diff_l4 = l4_a - l4_b

diff_l5 = l5_a - l5_b

然后计算RMSE：

RMSEs = [RMSE(diff_l2a, diff_l2b), RMSE(diff_l2a, diff_l4), RMSE(diff_l2a, diff_l5), ..., RMSE(diff_l4, diff_l5)]

相似之处：

average(RMSEs)

Answer 1

RMSE 相似度 DF 代码：

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error

num_objects = len(df)
similarity_matrix = np.zeros((num_objects, num_objects))

for i in range(num_objects):
    for j in range(i + 1, num_objects):
        rmse = np.sqrt(mean_squared_error(attributes[i], attributes[j]))
        similarity_matrix[i, j] = rmse
        similarity_matrix[j, i] = rmse

similarity_df = pd.DataFrame(similarity_matrix, columns=df['object'], index=df['object'])

我现在正在实现测试代码..