On Sat, Sep 27, 2014 at 10:37 AM, Darlan Cavalcante Moreira <
darc...@gmail.com> wrote:
>
> Some time ago I have reported a bug about the linalg.matrix_rank in
> numpy for complex matrices. This was quickly fixed and to take the
> advantage of the fix I'm now using numpy 1.9.0 installed through pip,
> instead of the version from my system (Ubuntu 14.04, with numpy version
> 1.8.1).
>
> However, I have now encountered a very strange bug in the SVD function,
> but only when numpy is manually installed (with pip in my case). When I
> calculate the SVD of complex matrices with more columns than rows the
> last rows of the returned V_H matrix are all equal to zeros. This does
> not happens for all shapes, but for the ones where this happens it will
> always happen.
>
> This can be reproduced with the code below. You can change the sizes of
> M and N and it happens for other sizes where N > M, but now all of them.
>
> --8<---------------cut here---------------start------------->8---
> import numpy as np
>
> M = 8 # Number of rows
> N = 12 # Number of columns
>
> # Calculate the SVD of a non-square complex random matrix
> [U, S, V_H] = np.linalg.svd(np.random.randn(M, N) + 1j*np.random.randn(M,
> N), full_matrices=True)
>
> # Calculate the norm of the submatrix formed by the last N-M rows
> if np.linalg.norm(V_H[M-N:]) < 1e-30:
> print("Bug!")
> else:
> print("No Bug")
> # See the N-M rows. They are all equal to zeros
> print(V_H[M-N:])
> --8<---------------cut here---------------end--------------->8---
>
> The original matrix can still be obtained from the decomposition, since
> the zero rows correspond to zero singular values due to the fact that
> the original matrix has more columns then rows. However, since the user
> asked for 'full_matrices' here (the default) returning all zeroes for
> these extra rows is not useful.
>
> In order to isolate the bug I tried installing some different numpy
> versions in different virtualenvs. I tried version 1.6, 1.7, 1.8.1 and
> 1.9 and the bug appears in all of then. Since it does not happen if I
> use the version 1.8.1 installed through the Ubuntu package manager, I
> imagine it is due to some issue when pip compiles numpy locally.
>
> Note: If I run numpy.testing.test() all tests are OK for all numpy
> versions I
> have tested. The only fail is a known fail and I get "OK (KNOWNFAIL=1)".
>
>
What does `np.__config__.show()` show?
Chuck
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