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> In : U Out : array () svd ( data ) # get the results SVD in descending order loadmat ( file_name ) > data = file_data > U, S, V = np. > import scipy.io as sio > file_name = 'a file location /Random_mat.Mat' > file_data = sio. Output singular values, left singular vectors, by plotting the right singular vectors compared.

In Python (A)obtain singular values / singular vectors of the matrix A in descending order by the.

In Matlab svds(A,k)obtain the largest k singular values / singular vectors of the matrix A by.

In this case, a comparison for the largest singular values and singular vectors according to it. Generated by the rand function of Matlab $ m \ times n, generate the m Verification of reproducibility in Scipy of Matlab of SVD (singular value decomposition).

has been resolved! Quick people who want to do the SVD of Matlab in Python to the following link.

But I was hoping to get the same decomposition, want to find out the cause for which can not be obtained.

And Matlab of SVD (singular value decomposition) examine the differences between the behavior of the Python of SVD.