import numpy
from numpy.linalg import pinv

m = 2
n = 3
A = numpy.random.randint(low=1, high=10, size=(m, n))
B = pinv(A)

if numpy.allclose(A, numpy.dot(A, numpy.dot(B, A))) and numpy.allclose(B, numpy.dot(B, numpy.dot(A, B))):
    print("We could find pseudoinverse of A")
else:
    print("We could not find pseudoinverse of A")

print("A: \n", A)
print("Pseudoinverse of A: \n", B)

Here, we are first creating a matrix A that contains random integers. We are using the function numpy.random.randint() for that purpose. The generated random integers will be within the range [low, high). And the size=(m, n) argument specifies that the created matrix will have m rows and n columns.

A = numpy.random.randint(low=1, high=10, size=(m, n))

Now, we are using the pinv() function from the numpy.linalg module to calculate the pseudoinverse of A.

B = pinv(A)

If B is indeed the pseudo inverse of A, then ABA = A and BAB = B. So, we are using the numpy.allclose() function to check the same.

if numpy.allclose(A, numpy.dot(A, numpy.dot(B, A))) and numpy.allclose(B, numpy.dot(B, numpy.dot(A, B))):
    print("We could find pseudoinverse of A")
else:
    print("We could not find pseudoinverse of A")

The output of the given program will be:

We could find pseudoinverse of A
A: 
 [[7 8 9]
 [5 7 6]]
Pseudoinverse of A: 
 [[ 0.14285714 -0.14285714]
 [-0.42857143  0.62857143]
 [ 0.38095238 -0.44761905]]