see below
On Tue, Jun 2, 2015 at 3:21 AM, Juan Pablo Carbajal <address@hidden> wrote:
On Mon, Jun 1, 2015 at 10:41 PM, Doug Stewart <address@hidden> wrote:
> I have a problem that has 8 independent variables and one output.
> We have taken 400 samples and now want to fit an equation to these data
> points.
> Some of the relation ships are nonlinear.
> Which octave function should I use to do the curve fitting?
>
>
> --
> DAS
>
>
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Doug, depending on the complexity of those relationships 400 samples
could be too little.
Can you compress the input space? (PCA or other embedding)
You can try kernel regression on your data using either gp_regress.
There is also octgpr (http://octave.sourceforge.net/octgpr/) package
I tried
pkg install -forge octgpr
gpr_predict.cc:26:30: error: ‘Octave_map’ does not name atype
octave_value getfield (const Octave_map& map, const char
So octgpr does not install with octave 4.0.0
or STK (http://kriging.sourceforge.net/htmldoc/). I haven't used any
of them so if you have comments or question it would be a nice
opportunity for me to take a look.
Finally there is gpml, it should be easy to use (but probably hard to
master) http://www.gaussianprocess.org/gpml/code/matlab/doc/
Again thanks for your's and others inputs.
I played with leasqr and came to a much better understanding of multi variable regression.
If you don't have enough data points then there are many equations that will fit the 8d data.
Also if you don't have the right data ponits then there are many equ. that fit.
By many eq. I mean the same relationships but different coefficients.
Think of only sampling the 8d space in one plain.
So we are looking at Taguchi methods of design of experiments, to decide on a set of measurement points, that minimally cover the region.
Then use these point with leasqr or other functions to do the curve fitting.
Thanks again.
Doug