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import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt import acquisition
def f(x):
return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)
if __name__ == '__main__':
acq = acquisition.UpperConfidenceBound(kappa=5) # kappa determines explore/Exploitation ratio
optimizer = BayesianOptimization(f=None, pbounds={'x': (-2, 2)}, verbose=2, random_state=1,
allow_duplicate_points=True)
optimizer.set_gp_params(normalize_y=True, alpha=2.5e-3, n_restarts_optimizer=20) # tuning of the gaussian parameters...
for point in range(20):
next_point_to_probe = optimizer.suggest()
NextPointValues = np.array(list(next_point_to_probe.values()))
mean,std = optimizer._gp.predict(NextPointValues.reshape(1, -1),return_std=True)
target = f(**next_point_to_probe)
optimizer.register(params=next_point_to_probe, target=target)
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