search

jamesdhope / regression-models

A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis
1 stars 2 forks source link

Using scipy's genetic algorithm for initial parameter estimation in gradient descent #1

Open zunzun opened 7 years ago

zunzun commented 7 years ago

I see you are writing Python code for gradient descent optimization on GitHub. A general problem for gradient descent, particularly for more complex equations, is the choice of initial parameters to start the "descent" in error space. Without good starting parameters, the algorithm will stop in a local error minimum. For this reason the authors of scipy have added a genetic algorithm for initial parameter estimation for use in gradient descent. 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 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

If you have any questions, please let me know. My background is in nuclear engineering and industrial radiation physics, and I love Python, so I will be glad to help.

James Phillips

jamesdhope commented 7 years ago

Thanks James. I'll take a look.