for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
import pytest
import random
import numpy as np
from ._parametrize import optimizers
def objective_function(para):
return -(para["x1"] + para["x1"])
search_space1 = {
"x1": np.array([1]),
"x2": np.arange(-10, 10, 1),
}
search_space2 = {
"x1": np.arange(-10, 10, 1),
"x2": np.array([1]),
objective_para = (
"search_space",
[
(search_space1),
(search_space2),
],
)
@pytest.mark.parametrize(*objective_para)
@pytest.mark.parametrize(*optimizers)
def test_best_results_0(Optimizer, search_space):
opt = Optimizer(search_space)
opt.search(objective_function, n_iter=30)
SimonBlanke /
Gradient-Free-Optimizers
