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From Italo Maia <italom...@hotmail.com>
Subject RE: [math]
Date Thu, 05 Jul 2012 18:16:11 GMT

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;


> Date: Thu, 5 Jul 2012 19:21:46 +0200
> From: gilles@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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