Unlock the Power of Matrix Multiplication with NumPy’s matmul()
Matrix Multiplication Made Easy
When it comes to performing matrix multiplication in NumPy, the matmul() method is the go-to solution. This powerful function allows you to multiply two matrices with ease, making it an essential tool for data scientists and engineers alike.
Understanding the Syntax
The syntax of matmul() is straightforward: np.matmul(first_matrix, second_matrix, out=None). Let’s break down the arguments:
first_matrix: The first matrix you want to multiply.second_matrix: The second matrix you want to multiply.out(optional): Specify a matrix where the result will be stored.
The Return Value
The matmul() method returns the matrix product of the input arrays. But what does this mean? Simply put, it means that the resulting matrix is the product of the input matrices.
Example 1: Multiply Two Matrices
When multiplying two matrices, it’s essential to remember that they must have a common dimension size. For instance, if we have A = (M x N) and B = (N x K), the resulting matrix C will be of size (M x K). Let’s see this in action:
“`
import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
C = np.matmul(A, B)
print(C)
“`
Example 2: Using the out Argument
In this example, we’ll create an output array called result using np.zeros() with the desired shape (2, 2) and data type int. We’ll then pass this result array as the out parameter in np.matmul(). The matrix multiplication is computed and stored in the result array.
“`
import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = np.zeros((2, 2), dtype=int)
np.matmul(A, B, out=result)
print(result)
“`
With matmul(), you’re just a few lines of code away from unlocking the power of matrix multiplication in NumPy.