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import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {
"x1": np.arange(0, 100, 0.1),
}
def test_verbosity_0():
opt = RandomSearchOptimizer(search_space)
opt.search(objective_function, n_iter=100, verbosity=False)
def test_verbosity_1():
opt = RandomSearchOptimizer(search_space,)
opt.search(
objective_function,
n_iter=100,
verbosity=["progress_bar", "print_results", "print_times"],
)
def test_verbosity_2():
verbosity=["print_results", "print_times"],
def test_verbosity_3():
verbosity=["progress_bar", "print_times"],
def test_verbosity_4():
verbosity=["progress_bar", "print_results"],
def test_verbosity_5():
opt.search(objective_function, n_iter=100, verbosity=[])
SimonBlanke /
Gradient-Free-Optimizers
