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: [email protected] > To: [email protected] > 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 > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] >
