[Numpy-discussion] lstsq: masked arrays, weights, scaling, and covariance

Charles R Harris charlesr.harris@gmail....
Sat Sep 17 14:50:02 CDT 2011


On Sat, Sep 17, 2011 at 1:40 PM, <josef.pktd@gmail.com> wrote:

> On Sat, Sep 17, 2011 at 2:52 PM, Charles R Harris
> <charlesr.harris@gmail.com> wrote:
> > Hi All,
> >
> > I'd like to start a discussion about modifications to lstsq to
> accommodate
> > the new masked arrays and move weights, scaling, and covariance
> > determination down to a lower common level. This is motivated by Travis'
> > recent changes to polyfit as well as my own various polynomial fits that
> > also allow weights. Also, once these features are pushed down to lstsq,
> it
> > should be possible to push them down further into a c-wrapper for the
> LAPACK
> > routines, which is where I really think they belong in the long run.
> >
> > Because missing values will effect the std/var/cov in the same way as
> > weights of zero, I think support for missing values and weights go
> naturally
> > together. Support for scaling and covariance are less closely tied, but
> they
> > are both features I use all the time in practice and having them
> available
> > will be useful.  It might also be nice to change the return signature,
> > though this would require a new function. I rather like the idea of
> > returning the coefficients and a dictionary, where everything not a
> > coefficient gets stuffed into the dictionary. In this regard see also
> Denis
> > Laxalde's proposal, something we might want to be consistent with.
> >
> > Thoughts?
>
> What's the speed penalty if we just want to use numpy/scipy linalg as
> a library and don't need any of the extra features?
>
> As some of the discussions have shown it can be pretty expensive to
> use linalg in loops.
>
>
I think the features should be optional and not the defaults, although
masked arrays will always need some special treatment.

Chuck
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