Create matrices of different sizes and ranks and use numpylinalgmatrix_rank to verify the returned matrices have the desired rank
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![Write a Python function that takes as input three integers, M, N, and r, and returns an MN matrix of rank r. Create matrices of different sizes and ranks, and use numpy.linalg.matrix_rank() to verify the returned matrices have the desired rank. Why should you expect a randomly generated matrix to have the rank you prescribe? What happens if you set r > min(M, N), and why does that happen? def randrank(M, N, r): A = random.randn(M, r) B = random.randn(N, r) return A.dot(B.T) R = randrank(32, 41, 7) print(R.shape, 'n') print(LA.matrix_rank(R), 'n') (32, 41) 7 (a) Use the function in Exercise #1 to generate a 5020 random matrix of rank r = 20, and visualize it. (b) Find the SVD of the matrix in part (a) using numpy.linalg.svd(). (c) Reconstruct the MN 2D array s with the entries of along its diagonal and zeros everywhere else: S = [s100000, s200000, sN00] if M > N, or S = [s1000, s2000, sM000000] if M < N (d) Verify that U.dot(S.dot(Vt)) recovers the matrix created in part (a). (e) Repeat (a) - (d) for a randomly generated 2050 matrix of rank r = 20.](https://gotit-pro.com/wp-content/uploads/2023/10/9d894064-eba6-456a-bd74-1f134ab849c5.png)
Write a Python function that takes as input three integers MN and r and returns an MN matrix of
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