> On Mar 28, 2019, at 7:54 AM, Madhavan Bomidi <blmadha...@gmail.com> wrote:
>
> Hi,
>
> I have x and y variables data arrays. These two variables are assumed to be
> related as y = A * exp(x/B). Now, I wanted to use Levenberg-Marquardt
> non-linear least-squares fitting to find A and B for the best fit of the
> data. Can anyone suggest me how I can proceed with the same. My intention is
> to obtain A and B for best fit.
>
Have you looked at the non-linear least-squares solutions in scicpy?
Specifically, a system I’ve had to solve several times in the past uses it and
it works quite well.
from scipy.optimize import curve_fit
def func2fit(x,a,b,c):
return a - b * np.exp(-c * x)
Bill
> Look forward to your suggestions and sample code as an example.
>
> Thanks and regards,
> Madhavan
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