Matrix Multiplication (@ Operator)

Hi Pythonistas!

We know @ operator is used for decorator, But there is another usage of @ operator i have recently learned. In this post we will learn about Matrix multiplication operators. The @ operator was introduced in PEP 465 (Python 3.5+) to provide a dedicated syntax for matrix multiplication, making code more readable and concise.

code

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = A @ B
print(result)

Output:

[[19 22]

 [43 50]]

This is equivalent to using np.dot(A, B) or A.dot(B), but the @ operator is cleaner and preferred for matrix multiplication.

Usage in Custom Classes

You can also define the behavior of the @ operator for your own classes by implementing the __matmul__ methods.

code

class Matrix:
    def __init__(self, data):
        self.data = data

    def __matmul__(self, other):
        result = [[sum(a * b for a, b in zip(row, col)) for col in zip(*other.data)] for row in self.data]
        return Matrix(result)

    def __repr__(self):
        return f"{self.data}"

A = Matrix([[1, 2], [3, 4]])
B = Matrix([[5, 6], [7, 8]])
result = A @ B
print(result)

Output:

[[19, 22], [43, 50]]

Matrix Multiplication: The @ operator is primarily used for matrix multiplication.

Custom Implementation: You can define the __matmul__ method to customize its behavior for your own classes.

Not for Scalars or Element-wise Operations: The @ operator is strictly for matrix multiplication. For element-wise operations, use * instead.

I hope you have learned something from this post. Please share your valuable suggestions with afsal@parseltongue.co.in