I opened https://github.com/scikit-learn/scikit-learn/issues/9528
I suggest to first error everywhere and then fix those for which it seems
easy and worth it, as Joel said, probably mostly in decomposition.
Though adding support even in a few places seems like dangerous feature
creep.
On 08/11/2017 03:16 AM, Raphael C wrote:
Although the first priority should be correctness (in implementation
and documentation) and it makes sense to explicitly test for inputs
for which code will give the wrong answer, it would be great if we
could support complex data types, especially where it is very little
extra work.
Raphael
On 11 August 2017 at 05:41, Joel Nothman <joel.noth...@gmail.com> wrote:
Should we be more explicitly forbidding complex data in most estimators, and
perhaps allow it in a few where it is tested (particularly decomposition)?
On 11 August 2017 at 01:08, André Melo <andre.nascimento.m...@gmail.com>
wrote:
Actually, it makes more sense to change
B = safe_sparse_dot(Q.T, M)
To
B = safe_sparse_dot(Q.T.conj(), M)
On 10 August 2017 at 16:56, André Melo <andre.nascimento.m...@gmail.com>
wrote:
Hi Olivier,
Thank you very much for your reply. I was convinced it couldn't be a
fundamental mathematical issue because the singular values were coming
out exactly right, so it had to be a problem with the way complex
values were being handled.
I decided to look at the source code and it turns out the problem is
when the following transformation is applied:
U = np.dot(Q, Uhat)
Replacing this by
U = np.dot(Q.conj(), Uhat)
solves the issue! Should I report this on github?
On 10 August 2017 at 16:13, Olivier Grisel <olivier.gri...@ensta.org>
wrote:
I have no idea whether the randomized SVD method is supposed to work
for
complex data or not (from a mathematical point of view). I think that
all
scikit-learn estimators assume real data (or integer data for class
labels)
and our input validation utilities will cast numeric values to float64
by
default. This might be the cause of your problem. Have a look at the
source
code to confirm. The reference to the paper can also be found in the
docstring of those functions.
--
Olivier
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