for testing and deploying your application
for finding and fixing issues
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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import pytest
import numpy as np
from gradient_free_optimizers import HillClimbingOptimizer
def parabola_function(para):
loss = para["x"] * para["x"] + para["y"] * para["y"]
return -loss
search_space = {
"x": np.arange(-10, 11, 1),
"y": np.arange(-10, 11, 1),
}
def test_epsilon_0():
epsilon = 1 / np.inf
opt = HillClimbingOptimizer(
search_space, initialize={"vertices": 1}, epsilon=epsilon
)
opt.search(parabola_function, n_iter=100)
search_data = opt.search_data
scores = search_data["score"].values
assert np.all(scores == -200)
SimonBlanke /
Gradient-Free-Optimizers
