Hi everyone,
does anyone know how I can use assumptions with find_fit? A minimal example would be
var('a,b,x')
assume(a>0,b>0)
find_fit([(2,2),(3,2.9),(4,4.1)],(a+1)/b*x,variables=[x],initial_guess=(-2,-2))
and the output is
[a == -105.66654155674746, b == -104.30685450333799]
Any help or hint on where to find a solution is appriciated. Thanks in advance.
You could fake it like this:
find_fit([(2,2),(3,2.9),(4,4.1)],(abs(a)+1)/abs(b)*x,variables=[x],initial_guess=(-2,-2))
So the best fit values of a,b are negative, but their absolute values are a "good fit" in your model.
Asked: 2012-06-21 16:24:13 +0200
Seen: 381 times
Last updated: Jun 23 '12
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Considering that `find_fit` uses Scipy and assumptions are in Maxima, I think it's quite unlikely that this would do much. I guess we could try to run them through assumptions, though this might slow it down appreciably.