本文介绍了numpy中的均方误差?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
numpy中是否有一种方法可以计算两个矩阵之间的均方误差?
Is there a method in numpy for calculating the Mean Squared Error between two matrices?
我尝试搜索,但没有找到.是另一个名字吗?
I've tried searching but found none. Is it under a different name?
如果没有,您如何克服呢?您是自己编写还是使用其他库?
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)
- 与
ax=0
一起,沿着行执行平均值,对于每一列,返回一个数组 - 与
ax=1
一起,沿着列执行平均值,对于每一行,返回一个数组 - 使用
ax=None
沿数组逐元素执行平均值,并返回标量值 - 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
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