[SciPy-User] simplex algorithm and curve fitting

josef.pktd@gmai... josef.pktd@gmai...
Tue Feb 21 04:47:49 CST 2012


On Tue, Feb 21, 2012 at 5:19 AM, servant mathieu
<servant.mathieu@gmail.com> wrote:
> Thanks a lot Joseph. I've got a last question which concerns initials guess
> for parameters. In fact, in many papers, i read " we fitted both functions
> using standard simplex optimization routines. This was repeated 10000 times
> with randomized initial values to avoid local minima."  The problemis the
> following: which range of values should we use for this randomization?

I wanted to mention this before, all fmin are local optimizers, anneal
is the only global optimizer in scipy, but a bit tricky to use. There
are other global optimizers written in python that might work better,
but I never tried any of these packages.

Choosing (random) starting values depends completely on the function
and there is no function independent recipe, since the
parameterization of a function is pretty arbitrary. So, you need an
"educated" guess over the possible range given the specific function
and problem.

For specific classes of functions in not too high dimension it would
be possible to find (and code) starting values, or ranges for a random
search.

I haven't tried out what your function looks like, but I would guess
that there are at least some sign restrictions. I usually try to see
if I can guess starting values, and ranges for randomization, based on
min, max and mean of the observations.

Josef

>
> Best,
> Mat
>
>
> 2012/2/21 <josef.pktd@gmail.com>
>
>> On Tue, Feb 21, 2012 at 4:38 AM, servant mathieu
>> <servant.mathieu@gmail.com> wrote:
>> > Dear Scipy users,
>> >
>> > I've got some troubles with the scipy.optimize.curve_fit function. This
>> > function is based on the Levenburg-Maquardt algorithm, which is
>> > extremely
>> > rapid but usually finds a local minimum, not a global one (and thus
>> > often
>> > returns anormal parameter values). In my research field, we usually use
>> > Nelder Mead's simplex routines to avoid this problem. However, I don't
>> > know
>> > if it is possible to perform curve fitting in scipy using simplex; the
>> > fmin
>> > function  doesn't seem to perform adjustments to data.
>> >
>> > Here is my code for fitting a three parameters hyperbolic cotangent
>> > function
>> > using curve_fit:
>> >
>> > from scipy.optimize import curve_fit
>> >
>> > import numpy as np
>> >
>> >
>> >
>> > def func (x, A,k, r ):
>> >
>> > return r + (A /(k*x)) * np.tanh (A*k*x)
>> >
>> >
>> >
>> > xdata = np.array ([0.15,0.25,0.35,0.45, 0.55, 0.75])
>> >
>> >
>> >
>> > datacomp = np.array ([344.3276300, 324.0051063, 314.2693475,
>> > 309.9906375,309.9251162, 307.3955800])
>> >
>> > dataincomp = np.array ([363.3839888, 343.5735787, 334.6013375,
>> > 327.7868238,
>> > 329.4642550, 328.0667050])
>> >
>> >
>> >
>> > poptcomp, pcovcomp = curve_fit (func, xdata, datacomp, maxfev = 10000)
>> >
>> > poptincomp, pcovincomp = curve_fit (func, xdata, dataincomp, maxfev =
>> > 10000)
>> >
>> >
>> >
>> >
>> >
>> > How could I proceed to perform the fitting using simplex?
>>
>> You need to define your own loss function for the optimizers like
>> fmin, or in future version minimize
>>
>> http://docs.scipy.org/doc/scipy-0.10.0/reference/tutorial/optimize.html#nelder-mead-simplex-algorithm-fmin
>>
>> something like this
>>
>> def loss(params, args)
>>     A, k, r = params
>>     y, x = args
>>     return ((y - func (x, A,k, r ))**2).sum()
>>
>> and use loss in the call to fmin
>>
>> Josef
>>
>
>
>
>
>
>>
>> >
>> >
>> >
>> > Best,
>> >
>> > Mat
>> >
>> >
>> > _______________________________________________
>> > SciPy-User mailing list
>> > SciPy-User@scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>> >
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>
>
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