how does multiplication differ for NumPy Matrix vs Array classes?

Question

The numpy docs recommend using array instead of matrix for working with matrices. However, unlike octave (which I was using till recently), * doesn't perform matrix multiplication, you need to use the function matrixmultipy(). I feel this makes the code very unreadable.

Does anybody share my views, and has found a solution?

Answer 1

The main reason to avoid using the matrix class is that a) it's inherently 2-dimensional, and b) there's additional overhead compared to a "normal" numpy array. If all you're doing is linear algebra, then by all means, feel free to use the matrix class... Personally I find it more trouble than it's worth, though.

For arrays (prior to Python 3.5), use dot instead of matrixmultiply.

E.g.

import numpy as np
x = np.arange(9).reshape((3,3))
y = np.arange(3)

print np.dot(x,y)

Or in newer versions of numpy, simply use x.dot(y)

Personally, I find it much more readable than the * operator implying matrix multiplication...

For arrays in Python 3.5, use x @ y.