SciPy Recipes

How to do it…

The SVD of a matrix can be calculated by using the svd function within scipy.linalg.

Let us understand how to code up using the svd function using the following example:

1. Initialize an array:

 a = np.array([[1, 2], [3, 4]]) 

2. Import the relevant packages and functions within the package:

from scipy import linalg 

3. Use the svd function to calculate the SVD matrices (U, Σ and V):

linalg.svd(a)

4. The output of the preceding function is as follows:

(array([[-0.40455358, -0.9145143 ],        [-0.9145143 ,  0.40455358]]), array([ 5.4649857 ,  0.36596619]), array([[-0.57604844, -0.81741556],        [ 0.81741556, -0.57604844]]))

​In order to further understand if the output is as per expectation, let us check if the multiplication of the individual ...