Assuming your initial values for your floating parameters are
reasonable, perhaps double check the partial derivatives with respect
to each of those parameters -- that's where I usually slip up.
For your function, I get
partial(y)/partial(Ai) = ( 1 - exp(-x/ti) )
partial(y)/partial(ti) = -Ai*x*exp(-x/ti) / ti^2
partial(y)/partial(Ap) = ( 1 - exp(-x/tp) )
partial(y)/partial(tp) = -Ap*x*exp(-x/tp) / tp^2
without trying them I don't guarantee that they're correct (as I
said, that's usually where I slip up), but perhaps this will point
you in the right direction. The signs of the second and fourth
partial derivatives are tedious, and a likely source of error.
Obviously you have to adapt this notation into your Fit_df function,
making sure to order the partials' indices according to the order of
the parameters in the vector of floats.
Good Luck
john
---------------------------------------------------------
Dr John Gehman ([EMAIL PROTECTED])
Research Fellow; Separovic Lab
School of Chemistry
University of Melbourne (Australia)
On 06/09/2006, at 1:06 AM, Petr Ent wrote:
hello,
I would like to fit data to following equation using non-lineat
least squares fitting. I am using real life data (not generated
from equation), but I have pretty good idea about final results and
I use it as seed values (starting values which you provide to the
solver). but whatever I do, solver does not converge.
equation:
y = fo + Ai * ( 1 - exp(-x/ti) ) + Ap * ( 1 - exp(-x/tp) )
where Ai, ti, Ap, tp are parameters, fo is constant. shape of the
function should be +- logarithmic, parameters should be similar to
Ai = 800
ti = 6
Ap = 200
tp = 15
I use modified non-linear least squares fitting example.
I tried to fit same source data using logarithmic and square-root
models
y = a * log (b * x) + c
y = a * sqrt(b * x) + c
and both of them converged and results are good.
If anyone has any clue, why the first model does not converge, or
what i am doing wrong, I would appreciate it.
regards Petr Ent
this is method where I use GSL to fitting
const gsl_multifit_fdfsolver_type *T;
gsl_multifit_fdfsolver *s;
int iter = 0;
gsl_multifit_function_fdf f;
double x_init[maxVars];
for (unsigned i = 0; i < configData[k].seeds.size(); i++)
{
x_init[i] = configData[k].seeds[i];
}
gsl_vector_view x = gsl_vector_view_array (x_init,
fitData.paramCount);
//Fit_... functions do what they are supposed to do, counting value
and derivations
//fitData is structure with all the info about fitting - data,
number of parameters, data length, ...
f.f = &Fit_f;
f.df = &Fit_df;
f.fdf = &Fit_fdf;
f.n = fitData.n;
f.p = fitData.paramCount;
f.params = &fitData;
T = gsl_multifit_fdfsolver_lmsder;
s = gsl_multifit_fdfsolver_alloc (T, fitData.n, fFunc->paramCount);
gsl_multifit_fdfsolver_set (s, &f, &x.vector);
do
{
iter++;
status = gsl_multifit_fdfsolver_iterate (s);
if (status)
{
break;
}
status = gsl_multifit_test_delta (s->dx, s->x,0,1e-2);
}
while (status == GSL_CONTINUE && iter < 500);
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