[SciPy-dev] parameter control in scipy.optimize.leastsq

[Robin]

On Sun, Oct 19, 2008 at 10:37 AM, Maximilian Fabricius <mxhf at gmx.net> wrote:
> Hello,
>
> I have been using scipy for about one and a half years now and have
> been extremely pleased.
>
> In this time I have made extensive use of scipy.optimize.leastsq.
> While I am generally fairly happy
> with its performance, I think it does lacks one important feature
> which I am used to from similar
> routines in other languages. (Please excuse if I have just missed that
> feature so far.)
>
> I would like to be able to control which parameters actually are
> fitted. For example I would like to be able to
> do
>
> leastsq( redsiduals, p0, args=(y), fit=([True,True,False,True]) )
>
> where the parameter "fit" would tell leastsq to leave the parameter
> p0[2] untouched while fitting the others.

I think you can do this by 'wrapping' your existing function:

Something like:

fit_function = lambda x, fixed:  residuals(x[0],x[1],fixed[0],x[2])

Then you can call least squared with the fixed argument specified
leastsq( fit_function, p0, args=(y,fixed))

You can either have the fixed parameters passed as a argument to the
lambda, or have them defined where the lambda is run (but I find it a
bit confusiong becuase the scoping gets funny). I'm sure you could do
something to get the [True, False] type notation you want - but since
you will have to specify the fixed values as well.

Robin