@ Python's Matrix Multiplication Operator
Posted by Aly Sivji in Quick Hits
2017 will forever be etched in our memories as the year Python overtook R to become the leading language for Data Science. There are many factors that play into this: Python's simple syntax, the fantastic PyData ecosystem, and of course buy-in from Python's BDFL.
PEP 465 introduced the @ infix operator that is designated to be used for matrix multiplication. The acceptance and implementation of this proposal in Python 3.5 was a signal to the scientific community that Python is taking its role as a numerical computation language very seriously.
I was a Computational Mathematics major in college so matrices are very near and dear to my heart. Shoutout to Professor Jeff Orchard for having us implement matrix algorithms in C++. His Numerical Linear Algebra course was the best class I've ever taken.
In this post, we will explore the @ operator.
import numpy as np
A = np.matrix('3 1; 8 2')
A
B = np.matrix('6 1; 7 9')
B
A @ B
Let's confirm this works
# element at the top left. i.e. (1, 1) aka (0, 0) in python
A[0, 0] * B[0, 0] + A[0, 1] * B[1, 0]
# element at the top right. i.e. (1, 2) aka (0, 1) in python
A[0, 0] * B[0, 1] + A[0, 1] * B[1, 1]
# element at the bottom left. i.e. (2, 1) aka (1, 0) in python
A[1, 0] * B[0, 0] + A[1, 1] * B[1, 0]
# element at the top right. i.e. (2, 2) aka (1, 1) in python
A[1, 0] * B[0, 1] + A[1, 1] * B[1, 1]
# let's put it in matrix form
result = np.matrix([[A[0, 0] * B[0, 0] + A[0, 1] * B[1, 0], A[0, 0] * B[0, 1] + A[0, 1] * B[1, 1]],
[A[1, 0] * B[0, 0] + A[1, 1] * B[1, 0], A[1, 0] * B[0, 1] + A[1, 1] * B[1, 1]]])
result
A @ B == result
Looks good!
The Python Data Model specifies that the @ operator invokes __matmul__ and __rmatmul__.
We can overload @ by defining custom behavior for each of the special methods above, but this would result in our API not being Pythonic.
To build Pythonic objects, observe how real Python objects behave.
- Luciano Ramalho, Author of Fluent Python
Comments