I know implementing the ParametricUnivariateFunction <https://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math4/analysis/ParametricUnivariateFunction.html> interface and overriding value and gradient methods can fit the custom curve function. But the gradient array has to be calculated for each special function. I want to know is it possible to calculate gradient array without the information of the function formula, just like SciPy curve_fit function ( https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html)? I have tried it in the MeteoInfo project using getJacobianFunction which calculates gradient array using UnivariateDifferentiableFunction and DerivativeStructure. And Jython was used to mimic the curve_fit function ( https://github.com/meteoinfo/MeteoInfo/blob/master/meteoinfo-lab/pylib/mipylib/numeric/optimize/minpack.py).
But I can not get the same result as SciPy curve_fit function. I nee your help, Thanks! Regards Yaqiang -- ************************************************* Dr. Yaqiang Wang Chinese Academy of Meteorological Sciences (CAMS) 46, Zhong-Guan-Cun South Avenue Beijing, 100081 China yaqiang.w...@gmail.com www.meteothink.org **************************************************
