for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {"x1": np.arange(-100, 101, 1)}
def _base_para_test_func(opt_para, optimizer):
opt = optimizer(search_space, **opt_para)
opt.search(
objective_function,
n_iter=30,
memory=False,
verbosity=False,
initialize={"vertices": 1},
)
para_key = list(opt_para.keys())[0]
para_value = getattr(opt, para_key)
assert para_value == opt_para[para_key]
SimonBlanke /
Gradient-Free-Optimizers
