Mean Squared Error in Numpy?
You can use:
mse = ((A - B)**2).mean(axis=ax)Or
mse = (np.square(A - B)).mean(axis=ax)- with
ax=0the average is performed along the row, for each column, returning an array - with
ax=1the average is performed along the column, for each row, returning an array - with
ax=Nonethe average is performed element-wise along the array, returning a scalar value
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_errormse = mean_squared_error(A, B)See Scikit Learn mean_squared_error for documentation on how to control axis.