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import numpy as np
from hyperactive import Hyperactive
def convex_function(pos_new):
score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
return score
search_space = {
"x1": list(np.arange(-100, 101, 0.1)),
"x2": list(np.arange(-100, 101, 0.1)),
}
def constraint_1(para):
# reject parameters where x1 and x2 are higher than 2.5 at the same time
return not (para["x1"] > 2.5 and para["x2"] > 2.5)
# put one or more constraints inside a list
constraints_list = [constraint_1]
hyper = Hyperactive()
# pass list of constraints
hyper.add_search(
convex_function,
search_space,
n_iter=50,
constraints=constraints_list,
)
hyper.run()
search_data = hyper.search_data(convex_function)
print("\n search_data \n", search_data, "\n")
SimonBlanke /
Hyperactive
