On Thu, Jul 05, 2012 at 06:16:11PM +0000, Italo Maia wrote:
>
> Some "context" below:
>
> Did you have a look at the classes in the package
>
> "org.apache.commons.math3.optimization" ?
>
> No, I did not. Let's see...
>
>
> Which function?
>
> This little devil:
>
> http://dpaste.com/hold/767050/
>
> public static double fnc(double t, double a, double b, double c){
> return Math.log(a) + b * Math.log(t) - c * t;
>
> }
>
> I have t in the matrix (first column). Second column are the observed values.
> I need to fit a, b and c.
> === END
>
> Well, the derivatives don't seem to be working.
>
> double da = 1/a;
> double db = b/t;
> double dc = -c;
>
Then try
1/a
log(t)
-t
Regards,
Gilles
>
> > Date: Thu, 5 Jul 2012 19:21:46 +0200
> > From: gil...@harfang.homelinux.org
> > To: user@commons.apache.org
> > Subject: Re: [math]
> >
> > Hi.
> >
> > >
> > > Thanks Giles! I was looking in the wrong place. Any suggestions on
> > > examples for these classes (a math function example would be very nice)?
> > > I've found this link (very helpful) but I don't know what to code in the
> > > gradient method. In ParametricUnivariateFunction.value I just returned my
> > > function output with the params as arguments (plus x). For gradient, I'm
> > > in a pitch.
> >
> > And I'm lacking context (sorry, I deleted your previous email from my
> > inbox)...
> >
> > Anyways, the "gradient(double x, double ... parameters)" method should
> > return the partial derivatives with respect to the _parameters_. So, for
> > example:
> > ---
> > public class ParamFuncExample implements ParametricUnivariateFunction {
> > public double value(double x, double ... p) {
> > return p[0] * x + p[1];
> > }
> >
> > public double[] gradient(double x, double ... p) {
> > return new double[] { x, 1 };
> > }
> > }
> > ---
> >
> >
> > HTH,
> > Gilles
> >
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