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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from hyperactive import Hyperactive
X, y = np.array([0]), np.array([0])
memory = False
n_iter = 25
def sphere_function(para, X_train, y_train):
loss = []
for key in para.keys():
if key == "iteration":
continue
loss.append(para[key] * para[key])
return -np.array(loss).sum()
search_config = {
sphere_function: {"x1": np.arange(-3, 3, 0.1), "x2": np.arange(-3, 3, 0.1)}
}
def test_epsilon():
for epsilon in [0.00001, 100]:
opt = Hyperactive(X, y, memory=memory)
opt.search(
search_config,
n_iter=n_iter,
optimizer={"HillClimbing": {"epsilon": epsilon}},
)
def test_n_neighbours():
for n_neighbours in [1, 100]:
optimizer={"HillClimbing": {"n_neighbours": n_neighbours}},
SimonBlanke /
Hyperactive
