Hi.
Le dim. 31 juil. 2022 à 18:05, Yaqiang Wang <yaqiang.w...@gmail.com> a écrit :
>
> Currently I just want to fit univariate function such as the following Python
> function:
>
> def func(x, a, b, c):
> return a * exp(-b * x) + c
>
>
> I also tried using SimpleCurveFitter to do it. MyParametricUnivariateFunction
> implements ParametricUnivariateFunction and overrides value and gradient
> methods
> (https://github.com/meteoinfo/MeteoInfo/blob/master/meteoinfo-math/src/main/java/org/meteoinfo/math/optimize/MyParametricUnivariateFunction.java),
> and the gradient array is calculated through numerical differentiation.
Given a list of points, { xi } (i = 0, 1, ... , N), and their associated values,
{ y_i }, "SimpleCurveFitter" aims at finding the best fit of an assumed
function "f"
yi = f(xi, p1, p2, p3, ...)
whose parameters p1, p2, p3, ... can be adjusted.
Internally, "SimpleCurveFitter" computes the Jacobian matrix
df(x0)/dp1 df(x0)/dp2 df(x0)/dp3 ...
df(x1)/dp1 df(x1)/dp2 df(x1)/dp3 ...
....
df(xN)/dp1 df(xN)/dp2 df(xN)/dp3 ...
needed by the "Levenberg-Marquardt" least-squares optimizer.
The gradient of "f" is an array where each slot contains the partial
derivative of the univariate function (at the given "x") wrt to each
_parameter_.
For your function above, the code would be (untested):
---CUT---
public class MyFunction implements ParametricUnivariateFunction {
public double value(double x, double ... parameters) {
final double a = parameters[0];
final double b = parameters[1];
final double c = parameters[2];
return a * Math.exp(-b * x) + c
}
public double[] gradient(double x, double ... parameters) {
final double a = parameters[0];
final double b = parameters[1];
final double c = parameters[2];
final double[] grad = new double[3];
grad[0] = Math.exp(-b * x);
grad[1] = -a * x * grad[0];
grad[2] = 1;
return grad;
}
}
---CUT---
HTH,
Gilles
> [...]
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