[SciPy-user] Fitting with global parameters optimize.leastsq
iain at day-online.org.uk.invalid
Wed Nov 8 14:04:52 CST 2006
Steve Schmerler wrote:
>>> 2) You're trying to fit all data sets (t == data_points, y ==
>>> raw_signals[:, i]) at once and I guess that won't work. You have to loop
>>> over A (or C or raw_signals[:, i]) building one data set and one initial
>>> guess (x0 = numpy.array([A[i], B, C[i]])) per loop.
>> That sounds similar to fitting each trace separately, and wouldn't give
>> you the global B?
> Well, yes. Taking a closer look at the cookbook example I see that this
> is indeed the right thing to do :)
> Although, I'm curious as to how this works if you don't fit 2 data sets
> but, say 100 with respect to local mimima in a such a high-dimensional
> search space.
That's beyond my little brain I'm afraid, but the results I'm getting
look good. I think you need pretty good initial guesses, but allowing
several linked (i.e. global) parameters does reduce the number of
degrees of freedom and hence the size of the search space.
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