[SciPy-User] curve_fit with float32 values
Sebastian Berg
sebastian@sipsolutions....
Fri Jan 18 04:36:42 CST 2013
On Fri, 2013-01-18 at 10:53 +0100, Sebastian Berg wrote:
> On Fri, 2013-01-18 at 09:18 +0100, Georg Brandl wrote:
> > Am 12.01.2013 15:58, schrieb Pauli Virtanen:
> > > 12.01.2013 14:54, Georg Brandl kirjoitti:
> > > [clip]
> > >> In the end (which took quite a while!) we found that the problem
> > >> was that the X data (which was directly loaded from a HDF file)
> > >> had a float32 dtype. This seems to confuse curve_fit. Same goes
> > >> for float16. float128 at least raises an exception. Integer types
> > >> seem fine given rounding, see the code/output below.
> > >
> > > AFAIK, a likely bug here is probably the choice of epsilon for numerical
> > > differentiation in leastsq --- the chosen epsilon is probably smaller
> > > than the machine epsilon for float32, hence problems appear.
> >
> > I had a look at the code now; minpack_lmdif calls
> >
> > ap_x = (PyArrayObject *)PyArray_ContiguousFromObject(x0, NPY_DOUBLE, 1, 1);
> >
> > IIUC this should already result in a float64 array, at least on machines
> > where double is 64-bits?
> >
>
> It may have been fixed, but I think the problem may be only about the
> temporaries within the function evaluation. You do not and maybe even
> cannot know if `args` contains a float32 array (you could check that the
> function return value is double, but even that may not tell the whole
> story).
>
To be exact, the thing is x0 gets typically unpacked in the evaluation
function. Which means that x0 is a set of scalars that have no effect on
the casting. You *could* hack around that... but...
> Sebastian
>
> > Georg
> >
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>
>
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