how do i pass in a model to be evaluated to DE?

[timm]

https://github.com/ai-se/Smotuned_FFT/blob/master/src/DE.py

[amritbhanu]

## Basic parameters for DE, Goal=choose Max, or Min. And if termination "Early" then DE would run to maximum of number of GEN, if "Late" termination it would run until there is no improvement in next generation.

de=DE(F=0.3, CR=0.7, NP=10, GEN=2, Goal="Max", termination="Early")
de.solve(fitness, paras=OrderedDict(), bounds=[], category=[], *r)

## fitness is the model or function name which returns the fitness value for which you are evaluating.
## paras = OrderedDict([("m",1), ("r",1),("k",1)]), tuning parameters for which you are optimizing.
## bounds= [(50,100,200, 400), (1,6), (5,21)], ranges of those parameters, even if its continuous or categorical.
## category=["categorical", "integer", "integer"], type of parameters. If categorical then it selects random element from the bound, if integer selects random integer from bound range, and continuous selects any real value from the bound range.
## all these 3 parameters should follow the same order in which they are meant to be.
## *r other parameters which are needed for fitness function

[timm]

got it