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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from gradient_free_optimizers import StochasticHillClimbingOptimizer
n_iter = 100
def get_score(pos_new):
return -(pos_new[0] * pos_new[0])
space_dim = np.array([10])
init_positions = [np.array([0]), np.array([1]), np.array([2]), np.array([3])]
def _base_test(opt, init_positions):
for nth_init in range(len(init_positions)):
pos_new = opt.init_pos(nth_init)
score_new = get_score(pos_new)
opt.evaluate(score_new)
for nth_iter in range(len(init_positions), n_iter):
pos_new = opt.iterate(nth_iter)
def _test_StochasticHillClimbingOptimizer(
init_positions=init_positions, space_dim=space_dim, opt_para={}
):
opt = StochasticHillClimbingOptimizer(init_positions, space_dim, opt_para)
_base_test(opt, init_positions)
def test_p_down():
for p_down in [0.0001, 100]:
opt_para = {"p_down": p_down}
_test_StochasticHillClimbingOptimizer(opt_para=opt_para)
SimonBlanke /
Gradient-Free-Optimizers
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