Metadata-Version: 2.1
Name: surfer
Version: 0.0.1
Summary: surF - Fourier surrogate modeling
Home-page: https://github.com/pypa/surfer
Author: Marco S. Nobile
Author-email: m.s.nobile@tue.nl
License: LICENSE.txt
Description: # surF - Surrogate Fourier modeling 
        
        surF is a novel surrogate modeling technique that leverages the discrete Fourier transform to generate a smoother, and possibly easier to explore, fitness landscape.
        
        ## Usage
        
        First of all, import surF as follows (please mind the upper case F):
        
        ```
        from surfer import surF
        ```
        
        Assume now that you have a fitness function ```f()``` defined over a search space ```hypercube```.
        
        In order to build a surrogate model with surF, considering ```gamma``` Fourier coefficients, built with ```sigma``` samples of the fitness landscape and interpolated with a grid with ```rho``` steps, use the following code:
        
        ```
        S = surF()
        S.specify_fitness(fitness)
        S.specify_search_space(hypercube)
        S.build_model(coefficients=gamma, numpoints=sigma, resolution=rho)
        ```
        
        Now, it is possible to exploit surF's  ```approximate(x)``` method to calculate the fitness value of a candidate solution ```x``` using the Fourier surrogate model.
        
        ## Citing surF
        
        If you find surF useful for your research, please cite our work as follows:
        
        Manzoni L., Papetti D.M., Cazzaniga P., Spolaor S., Mauri G., Besozzi D., and Nobile M.S.: Surfing on Fitness Landscapes: FST-PSO Powered by Fourier Surrogate Modeling (under revision)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
