I see you are writing regression code using Python. Scipy has solvers for non-linear equations, however scipy's default initial parameter values are all 1.0. This can be suboptimal, and in more complex equations often results in the algorithms finding a local minimum in error space. For this reason, the authors of scipy added a genetic algorithm for initial parameter estimation. The module is named scipy.optimize.differential_evolution.
I have used scipy's Differential Evolution genetic algorithm to determine initial parameters for fitting a double Lorentzian peak equation to Raman spectroscopy data of carbon nanotubes and found that the results were excellent. The GitHub project, with a test spectroscopy data file, is:
boyac
commented
7 years ago
I see you are writing regression code using Python. Scipy has solvers for non-linear equations, however scipy's default initial parameter values are all 1.0. This can be suboptimal, and in more complex equations often results in the algorithms finding a local minimum in error space. For this reason, the authors of scipy added a genetic algorithm for initial parameter estimation. The module is named scipy.optimize.differential_evolution.
I have used scipy's Differential Evolution genetic algorithm to determine initial parameters for fitting a double Lorentzian peak equation to Raman spectroscopy data of carbon nanotubes and found that the results were excellent. The GitHub project, with a test spectroscopy data file, is:
https://github.com/zunzun/RamanSpectroscopyFit
My background is in nuclear engineering and industrial radiation physics, and I love Python, so if you have any questions please let me know.
James Phillips