[SciPy-User] Fitting problem using optimize.fmin_slsqp

[Skipper Seabold]

On Thu, May 19, 2011 at 6:07 AM, guillaume gwenael <gwenln44 at gmail.com> wrote:
> Hello,
> I need to fit analytic data using a constrained least square fit. Here is
> the script :
> import numpy as npimport scipy as scp
>
> from scipy.optimize import fmin_slsqp
>
> def OriginalFunc(xaxis,a,b,c,d,e,f):
>
> p1=d*1e3
>
> p2=(a*e**2*c)/(p1*f)
>
> p3=(a*b*e)/f
>
> M=p3*np.sqrt( (1-1j*xaxis*p2)/(-1j*xaxis*p2) )
>
> N = ((e*xaxis)/b)*np.sqrt( (1-1j*xaxis*p2)/(-1j*xaxis*p2) )
>
> return M,p3,N
>
> def FirstOrderLinSyst(num,den,xaxis):
>
> j=np.complex(0,1)
>
> fos=num/(den-j*xaxis)
>
> return fos
>
> def PartialFractionExpansion(coefs,ref,xaxis):
>
> order=len(coefs)/2
>
> num=coefs[:order]
>
> den=coefs[order:]
>
> SumK=0.
>
> for k in range(order):
>
> fos=FirstOrderLinSyst(num[k],den[k],xaxis)
>
> SumK=SumK+fos
>
> approx=ref*SumK
>
> return approx
>
> def Residuals(fitcoefs,curve,ref,xaxis):
>
> diff=np.real(curve)-np.real(PartialFractionExpansion(fitcoefs,ref,xaxis))
>
> err=np.sqrt(
> np.sum(np.power(diff,2),axis=0)/np.sum(np.power(np.real(curve),2),axis=0) )
>
> return np.array([err],dtype=float)
>
> def GetParams4Fitting(init_fitcoefs,curve,ref,xaxis):
>
> pbounds=[(-np.inf,np.inf),(-np.inf,np.inf),(-np.inf,np.inf),(-np.inf,np.inf),
>
> (-np.inf,np.inf),(-np.inf,np.inf),(0,np.inf),(0,np.inf),(0,np.inf),
>
> (0,np.inf),(0,np.inf),(0,np.inf)]
>
> fitcoefs,fittedfunc,iters,imode,smode=fmin_slsqp(Residuals,
>
> init_fitcoefs,
>
> args=(curve,ref,xaxis),
>
> bounds=pbounds)
>
> return fitcoefs,fittedfunc,iters,imode,smode
>
> # Parameters
>
> a=1.204
>
> b=344.4256
>
> c=1.41
>
> d=10
>
> e=np.sqrt(3.5)
>
> f=0.5
>
> x_min=100
>
> x_max=5000
>
> step=100
>
> xaxis=2*np.pi*np.array([np.arange(x_min,x_max+step,step)])
>
> # Oiginal function
>
> M,p3,N=OriginalFunc(xaxis,a,b,c,d,e,f)
>
> # Initialization of fitting parameters
>
> order=6
>
> params_k=np.zeros((2*order))
>
> # Fitting parameters computing
>
> fitcoefs,fittedfunc,iters,imode,smode=GetParams4Fitting(params_k,M,p3,xaxis)
>
> It returnes the foolwing error:
> File "C:\Python26\lib\site-packages\scipy\optimize\slsqp.py", line 318, in
> fmin_slsqp g = append(fprime(x),0.0) File
> "C:\Python26\lib\site-packages\scipy\optimize\optimize.py", line 176, in
> function_wrapper return function(x, *args) File
> "C:\Python26\lib\site-packages\scipy\optimize\optimize.py", line 377, in
> approx_fprime grad[k] = (f(*((xk+ei,)+args)) - f0)/epsilon ValueError:
> setting an array element with a sequence.
>
> Can anyone help me?

Two things I noticed. The error message is because fmin_slsqp expects
func to return a scalar and then you want fmin_slsqp(...
full_output=1) to return all of those values.

Skipper