[SciPy-user] curve_fit step-size and optimal parameters
Mon Jun 8 15:19:43 CDT 2009
2009/6/8 Stéfan van der Walt <firstname.lastname@example.org>:
> 2009/6/8 Robert Kern <email@example.com>:
>> On Mon, Jun 8, 2009 at 14:59, ElMickerino<firstname.lastname@example.org> wrote:
>>> My question is, how can I get curve_fit to use a very small step-size for
>>> the phase, or put in strict limits, and to therefore get a robust fit. I
>>> don't want to tune the phase by hand for each of my 60+ datasets.
>> You really can't. I recommend the A*sin(w*t)+B*cos(w*t)
>> parameterization rather than the A*sin(w*t+phi) one.
> Could you expand? I can't immediately see why the second
> parametrisation is bad.
The cyclic nature of phi. It complicates things precisely as the OP describes.
> Can't a person do this fit using non-linear
> least-squares? Ah, that's probably why you use the other
> parametrisation, so that you don't have to use non-linear least
If you aren't also fitting the frequency, then yes. If you are fitting
for the frequency, too, the problem is still non-linear.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
More information about the SciPy-user