Comments (2)
I think there is no choice here. It bothers me that under-the-hood the SVD is the iterative SVD that can fail due to "lack of convergence" and this is a problem we've observed happens sometimes, as opposed to the rigorous factorization of a matrix which is always fine. But with current versions, scipy.pinv, scipy.pinv2, and numpy.pinv are all the same. No need to distinguish.
See the discussion scipy/scipy#8861 (comment)
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We need to remember this: if we end up with "SVD did not converge" errors we will need to add in a brute strength implementation using the dgesvd
SVD driver (accessible in scipy
).
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Related Issues (20)
- Can't install from pip HOT 3
- Support of missing values for generalized Procrustes analysis
- L-1 procrustes HOT 10
- Installing with pip according to instructions does not work HOT 5
- Molecular Structure Distance HOT 1
- Non square data in softassign function - cannot reshape array HOT 4
- An alternative way of solving rotational Procrutes problem HOT 4
- Web Site HOT 2
- Would be nice to have the used scaling and translation returned in the answer HOT 9
- Add support of 1D, 2D and 3D molecular structures HOT 3
- translation, scling, weighting for positive semi-definite Procrustes problem HOT 2
- Update the summary table when #98 is done
- Failing testing, `test_psdp_woodgate_non_full_rank` HOT 11
- More algorithms on `PSDP` HOT 18
- Another round of code review for GSoC HOT 1
- pinv2 deprecated HOT 12
- Support Weight Matrices
- One-Sided Procrustes for Toeplitz matrices HOT 5
- positive semi-definite Procrustes problem HOT 6
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