Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-12 Thread Alexandre Gramfort
I agree with Gaël on this. If you want to support complex values just copy the estimators / functions you want and maintain them in a separate package. +1 to error when complex are passed. ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-11 Thread Gael Varoquaux
On Fri, Aug 11, 2017 at 12:37:12PM -0400, Andreas Mueller wrote: > 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

Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-11 Thread Andreas Mueller
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

Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-11 Thread Raphael C
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

Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-10 Thread Joel Nothman
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 wrote: > Actually, it makes more sense to change > > B =

Re: [scikit-learn] Truncated svd not working for complex matrices

2017-08-10 Thread Olivier Grisel
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