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?
You can use:
mse = ((A - B)**2).mean(axis=ax)
Or
mse = (np.square(A - B)).mean(axis=ax)
ax=0
the average is performed along the row, for each column, returning an arrayax=1
the average is performed along the column, for each row, returning an arrayax=None
the average is performed element-wise along the array, returning a scalar valueThis 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.
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