On 01/09/2014 10:45 AM, Matthias Sitte wrote:
On 01/09/2014 11:22 AM, Patrick Alken wrote:
You'll want to use the gsl_multifit_linear or gsl_multifit_robust
routines in Least Squares -> multi parameter fitting part of the manual.
You need to build the least squares matrix manually and then pass it to
those routines.
Ok, so solve y = X.c I use c=(c00, c10, c01, c20, c11, c02) and for each
data point (px,py,pz) I add a line to the matrix X with (1, px, py,
px*px, px*py, py*py) and a line the vector y with value pz, right?
Finally, I end up with a linear matrix-vector equation, where y has
(possibly) large number N of rows, X is an N-by-6 matrix, and the result
is a coefficient vector of length 6, right? Plus I get the 6-by-6
covariance matrix and the chi^2.
If so, that's way easier than I thought, but it's GSL 8-) Nice interface!
Thx!
Matthias
Yes that's correct. If you have a lot of noise/outliers in your dataset
try the robust fitting routine, otherwise the linear/ordinary least
squares routine will work fine.
On 01/09/2014 10:15 AM, Matthias Sitte wrote:
Hi,
I'm looking for a way to to a least-square-fit to a data set (x,y,z).
The fit function should be a polynomal like the following:
z(x,y) = \sum_{m,n=0,m+n<=N}^{N} c_{mn} x^m y^n
= c_{00} + c_{10} x + c_{01} y
+ c_{20} x^2 + c_{11} xy + c_{02} y^2 + ...
I've been using GSL a lot and know my way around, but I don't know the
terminology of least-square-fits, so I'm kindly asking you to name the
proper function(s) I should use before I lose myself in the docs ;-)
Thx,
Matthias
