Today I learned that Python 3.5+ supports the operator
@ for matrix multiplication.
You can try to use it with just vanilla Python,
but no vanilla Python types define their behaviour with
>>> 3 @ 5 Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: unsupported operand type(s) for @: 'int' and 'int'
However, just looking at the error above, you see that the error is in
not knowing what to do with integers.
The error is not the fact that
@ is an invalid operator!
If you have
numpy at hand, you can check
numpy arrays added support to be used with
>>> import numpy as np >>> np.random.rand(3, 3) @ np.random.rand(3, 3) array([[0.89431673, 0.57949659, 0.59470797], [0.47364302, 0.29837518, 0.33552972], [1.12634752, 0.75218169, 0.78876082]]) >>> _ @ np.eye(3) # The identity (eye) matrix leaves the other matrix unchanged. array([[0.89431673, 0.57949659, 0.59470797], [0.47364302, 0.29837518, 0.33552972], [1.12634752, 0.75218169, 0.78876082]])
_ is just a way to refer to the last result of the REPL.
Read about it in this Pydon't.
@with custom classes/types
If you want your own objects to add support for
all you have to do is implement the dunder methods
>>> class Dummy: ... def __matmul__(self, other): ... print("Works!") ... return 42 ... def __rmatmul__(self, other): ... print("Also works!") ... return 73 ... >>> d = Dummy() >>> d @ 1 Works! 42 >>> 1 @ d Also works! 73
There's also the
__imatmul__ method for in-place matrix multiplication:
>>> class Dummy: ... def __imatmul__(self, other): ... print("In-place!") ... >>> d = Dummy() >>> d @= 1 In-place!
Of course, this silly example above doesn't show you the proper semantics of the
It just shows you they exist and they interact with the operator
By the way, for reference, here is the tweet that showed me this:
How did I not know @ was a proper operator?!— Rodrigo 🐍📝 (@mathsppblog) October 12, 2021
That's it for now! Stay tuned and I'll see you around!
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