An alternative solution for Q.82

[iamyifan]

82. Compute a matrix rank (★★★) hint: np.linalg.svd

# Author: Stefan van der Walt

Z = np.random.uniform(0,1,(10,10)) U, S, V = np.linalg.svd(Z) # Singular Value Decomposition rank = np.sum(S > 1e-10) print(rank)

numpy.linalg.matrix_rank Doc provides an alternative way to compute matrix rank.

The alternative solution will be:

from numpy.linalg import matrix_rank

Z = np.random.uniform(0,1,(10,10))
print(matrix_rank(Z))

[rougier]

Not sure to see the link between the question and your answer.

[iamyifan]

Not sure to see the link between the question and your answer.

My bad. I've updated Q.82 in my last comment.

[rougier]

Should I close this issue then?