Metadata-Version: 2.1
Name: GPGO
Version: 0.1
Summary: Bayesian Optimization with Gaussian Process as surrogate model
Home-page: https://github.com/FNTwin/GPGO
Author: Cristian Gabellini
License: MIT
Download-URL: https://github.com/FNTwin/GPGO/archive/BayOpt.tar.gz
Description: # GPGO - Gaussian Process GO
        
        My own implementation of Bayesian Black box Optimization with Gaussian Process as a surrogate model.
        It is still in development as I'm using it for my Master degree thesis to achieve a bottom up optimization of the Dissipative
        Particle Dynamics force field for a complex system of polymers chains functionalized gold nanoparticles in a water solvent. 
        
        # Maximizing the Acquisition function (EI only for now)
        In this little package right now there are 3 ways to run an optimization task with Gaussian Processes:
        
        -NAIVE : AkA sampling the acquisition function with a grid of some kind or a quasi random methods as LHS
        
        -BFGS : Find the Maxima of the Acquisition function by using the L-BFGS-B optimizer
        
        -DIRECT : Find the Maxiam of the Acquisition function by using the DIRECT optimizer (need the DIRECT python package)
        
        # TODO
        
        -An integration with LAMMPS using the pyLammps routine
        
        -Tutorials and Examples
        
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/markdown
