Numpy 中的均方误差? [英] Mean Squared Error in Numpy?

问题描述

numpy 中是否有计算两个矩阵之间的均方误差的方法?

Is there a method in numpy for calculating the Mean Squared Error between two matrices?

我尝试过搜索，但没有找到.是用不同的名字吗?

I've tried searching but found none. Is it under a different name?

如果没有，你如何克服这个问题?你是自己写的还是使用不同的库?

If there isn't, how do you overcome this? Do you write it yourself or use a different lib?

推荐答案

您可以使用:

mse = ((A - B)**2).mean(axis=ax)

或

mse = (np.square(A - B)).mean(axis=ax)

• with ax=0 沿行执行平均值，对于每一列，返回一个数组
• with ax=1 沿列执行平均值，对于每一行，返回一个数组
• 使用 ax=None 沿数组按元素执行平均值，返回标量值
• with ax=0 the average is performed along the row, for each column, returning an array
• with ax=1 the average is performed along the column, for each row, returning an array
• with ax=None the average is performed element-wise along the array, returning a scalar value

这篇关于Numpy 中的均方误差?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！