Model and experiment fitting.
Sebastian Żurek
sebzur at pin.if.uz.zgora.pl
Sat Oct 21 09:00:27 CDT 2006
A. M. Archibald napisał(a):
>
> In scipy there are some very convenient spline fitting tools which
> will allow you to fit a nice smooth spline through the simulation data
> points (or near, if they have some uncertainty); you can then easily
> look at the RMS difference in the y values. You can also, less easily,
> look at the distance from the curve allowing for some uncertainty in
> the x values.
>
I'll try a spline fitting. I've already made some linear interpolations
(see Robert Kern answer) which works well enough to use it. I'm working
on a genetic algorithms application to the model parameters
optimalization problem and this RMSe comparison serves me as 'fitness
function'. This 'fitness function' is important element in whole
procedure, so I'm trying to found the best solution to obtain it.
> I suppose you could also fit a curve through the experimental points
> and compare the two curves in some way.
>
Well, I can do it, indeed. But every single fitting procedure implicate
some additional error, so when it comes to fit, I must use it very
cautiously. The simulated data-points fitting should be the only
acceptable fitting procedure, I guess.
> If you want to avoid using an a priori model, Numerical Recipes
> discuss some possible approaches ("Do two-dimensional distributions
> differ?" at http://www.nrbook.com/a/bookcpdf.html is one) but it's not
> clear how to turn the problem you describe into a solvable one - some
> assumption about how the models vary between sampled x values appears
> to be necessary, and that amounts to interpolation.
>
I'll look to this NR discussion.
Thank You for these comments!
Sebastian
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