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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import pytest
import numpy as np
from gradient_free_optimizers import StochasticHillClimbingOptimizer
n_iter = 1000
def objective_function(para):
score = -para["x1"] * para["x1"]
return score
search_space = {
"x1": np.arange(0, 10, 1),
}
def test_p_accept():
p_accept_low = 0.5
p_accept_high = 1
epsilon = 1 / np.inf
opt = StochasticHillClimbingOptimizer(
search_space,
p_accept=p_accept_low,
epsilon=epsilon,
initialize={"random": 1},
)
opt.search(objective_function, n_iter=n_iter)
n_transitions_low = opt.n_transitions
p_accept=p_accept_high,
n_transitions_high = opt.n_transitions
print("\n n_transitions_low", n_transitions_low)
print("\n n_transitions_high", n_transitions_high)
lower_bound = int(n_iter * p_accept_low)
lower_bound -= lower_bound * 0.1
higher_bound = n_iter
assert lower_bound < n_transitions_low < n_transitions_high < higher_bound
SimonBlanke /
Gradient-Free-Optimizers
