Numpy 中的均方误差？

Question

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

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

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

Answer 1

You can use:

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

Or

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

• 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

Answer 2

This isn't part of numpy , but it will work with numpy.ndarray objects. A numpy.matrix can be converted to a numpy.ndarray and a numpy.ndarray can be converted to a numpy.matrix .

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(A, B)

See Scikit Learn mean_squared_error for documentation on how to control axis.

Answer 3

更麻木

np.square(np.subtract(A, B)).mean()

Answer 4

Another alternative to the accepted answer that avoids any issues with matrix multiplication:

 def MSE(Y, YH):
     return np.square(Y - YH).mean()

From the documents for np.square :

Return the element-wise square of the input.

Answer 5

只是为了踢

mse = (np.linalg.norm(A-B)**2)/len(A)

Answer 6

The standard numpy methods for calculation mean squared error (variance) and its square root (standard deviation) are numpy.var() and numpy.std() , see here andhere . They apply to matrices and have the same syntax as numpy.mean() .

I suppose that the question and the preceding answers might have been posted before these functions became available.