[SciPy-User] Curve fitting questions
Tue Oct 19 14:20:46 CDT 2010
On Tue, Oct 19, 2010 at 1:04 PM, <firstname.lastname@example.org> wrote:
> I think you could take logs and you would have a linear function in
> param=log(x), and you could use linalg to solve for param, and then
> transform back exp(param). Or this would give you a starting value if
> you want the non-linear optimization.
Using 3 and 5 data-points the curve_fit usually does a good job, even without
the initial estimates provided. When it's necessary we usually constrain the
initial parameters with max CCN concentration for C (param) and a typical
k (param) values.
This even works with 2 data-points:
I: ccn_ss1 = [0.27, 0.34]
I: ccn_conc1 = np.array([383.51237409766452, 424.82669523141652])
I: tfit2, pcov2 = curve_fit(my_ck, ccn_ss1, ccn_conc1, p0=(424,
provides me reasonable estimations. However, having another data-point
improve the quality of the fit and estimations.
>> ccn_ss1 = 0.27
>> ccn_conc1 = 383.51237409766452
>> # One data point estimation fails with IndexError: index out of range for array
>> tfit3, pcov3 = curve_fit(my_ck, ccn_ss1, ccn_conc1, p0=tfit1, ftol=1)
> If you have one parameter to estimate and only one observations, then
> you should be able to solve it exactly with one of the solvers/
> rootfinders in scipy. optimize.
I want to estimate two parameters using one observation (which is a
data-pair for my case --one for ccn_ss1 and one for ccn_conc1.)
Probably, in this current version fsolve can't do give me any roots.
def my_ck(x, a, b):
fsolve(my_ck, x0=tfit1, args=(ccn_ss1, ccn_conc1), xtol=1)
rather gives a couple of overflow warnings:
Warning: overflow encountered in power
In one data-pair situation my function looks like:
a*x**b = 383.5
Now there are two unknowns providing the x as ccn_ss1 as a*0.27**b =
383.5. I should make one more assumption otherwise it is still
unsolvable. Probably making an assumption for a, then I can hand solve
this easily. OK, with a = 350 assumption in 350*0.27**b == 383.5, here
solving for b results ~ -0.065
With some modifications on the original fitfunc:
return 350*0.27**x - 383
fsolve nicely estimates what I want.
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