In this issue the progress for the API-changes for v2 is shown.
The objective-function parameter might make more sense in the optimizer-class instead of the search-method. Parameters in the method should be data you would want to change from one search to the next. It does not make sense to me to change the objective-function but not the search-space. So the new API-design would look like this:
import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def parabola_function(para):
loss = para["x"] * para["x"]
return -loss
search_space = {"x": np.arange(-10, 10, 0.1)}
opt = RandomSearchOptimizer(parabola_function, search_space)
opt.search(n_iter=100000)
since there are multiple kinds of "powell's methods", the existing "powell's method" in GFO will be renames to "Powell's Conjugate Direction Method" in v2.0
In this issue the progress for the API-changes for v2 is shown.
The objective-function parameter might make more sense in the optimizer-class instead of the
search
-method. Parameters in the method should be data you would want to change from one search to the next. It does not make sense to me to change the objective-function but not the search-space. So the new API-design would look like this: